View RespectKnowledge's full-sized avatar
28 followers · 0 following

Achievements

Achievement: StarstruckAchievement: Arctic Code Vault Contributor

Achievements

Achievement: StarstruckAchievement: Arctic Code Vault Contributor

Block or report RespectKnowledge

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Content in all repositories owned by your account will be closed.
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
RespectKnowledge/README.md

🔬 I’m currently working at the intersection of Deep Learning and Medical Imaging, developing advanced models for segmentation, classification, and beyond.

🧠 My focus includes self-supervised, semi-supervised, and generative learning techniques to tackle real-world challenges where annotated data is scarce or noisy.

🏗️ I’m also exploring the design and training of foundation models for scalable and generalizable medical image understanding across modalities and tasks.

🤝 I'm keen to collaborate on innovative research and translational projects, particularly those with industry relevance and clinical impact.

🏆 MICCAI 2024 Challenge Contributions

🏅 PositionChallenge NameTitle/Method
🥇 1stAIMS-TBI - Automated Identification of Moderate-Severe Traumatic Brain Injury LesionsLeveraging Student-Teacher Networks in Self-Supervised Learning for Enhanced TBI Severity Segmentation
4thISLES - Ischemic Stroke Lesion SegmentationA Two-Stage SSL Approach for Ischemic Stroke Lesion Segmentation
🥈 2ndUWF4DR - Ultra-Widefield Fundus Imaging for Diabetic RetinopathyEfficient Deep Learning for Ultra-Widefield Fundus Imaging
🥈 2ndFETA - Fetal Tissue AnnotationPseudo Labeling + 3D Deep Learning Models
🥉 3rdMBH-Seg - Multi-class Brain Hemorrhage SegmentationEfficient SSL-Based Deep Learning for Hemorrhage Segmentation
🥉 3rdCARE - Real World Medical Image AnalysisTwo-Stage SSL for Whole Heart Segmentation in CT and MRI
4thCURVAS - Calibration and Uncertainty for Multi-Rater Volume AssessmentxSLTM-UNet Deep Learning Model
4thTriALS24 - Triphasic-Aided Liver Lesion SegmentationSSL-Based Student-Teacher Architecture for Non-Contrast CT
5thHNTSMRG - Head and Neck Tumor Segmentation in MRSelf-Supervised xLSTM-UNet for Tumor Segmentation
5thMBAS - Multi-class Bi-Atrial SegmentationStudent-Teacher SSL for 3D Bi-Atrial Segmentation
8thMARIO - AMD Progression in OCTEfficient DL Models for Age-Related Macular Degeneration Tracking
9thCOSAS - Cross-Organ and Scanner Adenocarcinoma SegmentationSwin-UNet + Parallel Cross-Attention
9thTopCoW - Anatomical Segmentation of the Circle of WillisPretrained 3D Segmentation Models
12thAortaSeg24 - Aortic Branch and Zone SegmentationSSL-Based Aortic Segmentation with Student-Teacher Architectures

🧠 IEEE ISBI 2023 Challenge Contributions

🏅 PositionChallenge Name
🥇 1stCuRIOUS 2022 - Image Registration & Segmentation
4thCMRxMotion Challenge
4thcSeg-2022 - Infant Cerebellum MRI Segmentation
6thATM’22 - Multi-Site Multi-Domain Airway Tree Modeling
9thNCCT - Intracranial Hemorrhage Segmentation
10thISLES'22 - Ischemic Stroke Lesion Segmentation
11thKidney Parsing Challenge - Renal Cancer Treatment
16thPulmonary Artery Segmentation Challenge

🔹 MICCAI 2022 Challenge Contributions

🏅 PositionChallenge NameMethod Summary
🥇 1stCuRIOUS – Correction of Brain Shift with Intra-Operative UltrasoundSelf-Supervised Two-Stage 3D ResUNet
4thCMRxMotion Challenge–
4thcSeg-2022 – Infant Cerebellum MRI Segmentation–
6thATM’22 – Multi-site, Multi-Domain Airway Tree Modeling3D Deep Learning Models
9thNCCT – Intracranial Hemorrhage Segmentation–
10thISLES'22 – Ischemic Stroke Lesion Segmentation–
11thKidney Parsing Challenge – Renal Cancer Treatment–
16thPulmonary Artery Segmentation Challenge–

🧠 MICCAI 2021 Challenge Contributions

🏅 PositionChallenge Name
4thDiabetic Foot Ulcer Challenge
4thFetReg - Placental Vessel Segmentation in Fetoscopy
5thFoot Ulcer Segmentation Challenge
7thFLARE - Fast and Low GPU Abdominal Organ Segmentation
10thRight Ventricular Segmentation in Cardiac MRI
13thFeta2021 - Fetal Brain Tissue Segmentation
6thChest XR COVID-19 Detection (Grand Challenge)
13thAIROGS - Robust Glaucoma Screening
5thKNIGHT Challenge - Kidney Clinical Notes & Imaging Biomarker Discovery
14thHECKTOR - Head and Neck Tumor PET/CT Segmentation

Pinned Loading

  1. EMIDEC-ChallengeEMIDEC-ChallengePublic

    Python 1

  2. LSTM-1DCNN-GRU-for-DepressionLSTM-1DCNN-GRU-for-DepressionPublic

    Jupyter Notebook 16 4

  3. EEG_Speech_Depression_MultiDLEEG_Speech_Depression_MultiDLPublic

    Jupyter Notebook 39 7

  4. ABIDE_Classification_DLModelABIDE_Classification_DLModelPublic

    Python 6 3

  5. FLARE_21_Segmentation_DLFLARE_21_Segmentation_DLPublic

    Python 4

  6. EEG-using-deep-LearningEEG-using-deep-LearningPublic

    In this Basic Tutorial, I have used 1DCNN for EEG classification using random dataset, You can use your own dataset

    Jupyter Notebook 19 1

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
View RespectKnowledge's full-sized avatar
28
followers
 · 0
following

Achievements
Achievement: StarstruckAchievement: Arctic Code Vault Contributor
Achievements
Achievement: StarstruckAchievement: Arctic Code Vault Contributor

Block or report RespectKnowledge

Block user

Prevent this user from interacting with your repositories and sending you notifications.
Learn more about blocking users.


You must be logged in to block users.


Content in all repositories owned by your account will be closed.


Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.

Report abuse

Contact GitHub support about this user’s behavior.
Learn more about reporting abuse.

Report abuse
RespectKnowledge/README.md
🔬 I’m currently working at the intersection of Deep Learning and Medical Imaging, developing advanced models for segmentation, classification, and beyond.
🧠 My focus includes self-supervised, semi-supervised, and generative learning techniques to tackle real-world challenges where annotated data is scarce or noisy.
🏗️ I’m also exploring the design and training of foundation models for scalable and generalizable medical image understanding across modalities and tasks.
🤝 I'm keen to collaborate on innovative research and translational projects, particularly those with industry relevance and clinical impact.
🏆 MICCAI 2024 Challenge Contributions
🏅 PositionChallenge NameTitle/Method
🥇 1stAIMS-TBI - Automated Identification of Moderate-Severe Traumatic Brain Injury LesionsLeveraging Student-Teacher Networks in Self-Supervised Learning for Enhanced TBI Severity Segmentation
4thISLES - Ischemic Stroke Lesion SegmentationA Two-Stage SSL Approach for Ischemic Stroke Lesion Segmentation
🥈 2ndUWF4DR - Ultra-Widefield Fundus Imaging for Diabetic RetinopathyEfficient Deep Learning for Ultra-Widefield Fundus Imaging
🥈 2ndFETA - Fetal Tissue AnnotationPseudo Labeling + 3D Deep Learning Models
🥉 3rdMBH-Seg - Multi-class Brain Hemorrhage SegmentationEfficient SSL-Based Deep Learning for Hemorrhage Segmentation
🥉 3rdCARE - Real World Medical Image AnalysisTwo-Stage SSL for Whole Heart Segmentation in CT and MRI
4thCURVAS - Calibration and Uncertainty for Multi-Rater Volume AssessmentxSLTM-UNet Deep Learning Model
4thTriALS24 - Triphasic-Aided Liver Lesion SegmentationSSL-Based Student-Teacher Architecture for Non-Contrast CT
5thHNTSMRG - Head and Neck Tumor Segmentation in MRSelf-Supervised xLSTM-UNet for Tumor Segmentation
5thMBAS - Multi-class Bi-Atrial SegmentationStudent-Teacher SSL for 3D Bi-Atrial Segmentation
8thMARIO - AMD Progression in OCTEfficient DL Models for Age-Related Macular Degeneration Tracking
9thCOSAS - Cross-Organ and Scanner Adenocarcinoma SegmentationSwin-UNet + Parallel Cross-Attention
9thTopCoW - Anatomical Segmentation of the Circle of WillisPretrained 3D Segmentation Models
12thAortaSeg24 - Aortic Branch and Zone SegmentationSSL-Based Aortic Segmentation with Student-Teacher Architectures
🧠 IEEE ISBI 2023 Challenge Contributions
🏅 PositionChallenge Name
🥇 1stCuRIOUS 2022 - Image Registration & Segmentation
4thCMRxMotion Challenge
4thcSeg-2022 - Infant Cerebellum MRI Segmentation
6thATM’22 - Multi-Site Multi-Domain Airway Tree Modeling
9thNCCT - Intracranial Hemorrhage Segmentation
10thISLES'22 - Ischemic Stroke Lesion Segmentation
11thKidney Parsing Challenge - Renal Cancer Treatment
16thPulmonary Artery Segmentation Challenge
🔹 MICCAI 2022 Challenge Contributions
🏅 PositionChallenge NameMethod Summary
🥇 1stCuRIOUS – Correction of Brain Shift with Intra-Operative UltrasoundSelf-Supervised Two-Stage 3D ResUNet
4thCMRxMotion Challenge–
4thcSeg-2022 – Infant Cerebellum MRI Segmentation–
6thATM’22 – Multi-site, Multi-Domain Airway Tree Modeling3D Deep Learning Models
9thNCCT – Intracranial Hemorrhage Segmentation–
10thISLES'22 – Ischemic Stroke Lesion Segmentation–
11thKidney Parsing Challenge – Renal Cancer Treatment–
16thPulmonary Artery Segmentation Challenge–
🧠 MICCAI 2021 Challenge Contributions
🏅 PositionChallenge Name
4thDiabetic Foot Ulcer Challenge
4thFetReg - Placental Vessel Segmentation in Fetoscopy
5thFoot Ulcer Segmentation Challenge
7thFLARE - Fast and Low GPU Abdominal Organ Segmentation
10thRight Ventricular Segmentation in Cardiac MRI
13thFeta2021 - Fetal Brain Tissue Segmentation
6thChest XR COVID-19 Detection (Grand Challenge)
13thAIROGS - Robust Glaucoma Screening
5thKNIGHT Challenge - Kidney Clinical Notes & Imaging Biomarker Discovery
14thHECKTOR - Head and Neck Tumor PET/CT Segmentation

Pinned
Loading
  1. EMIDEC-ChallengeEMIDEC-ChallengePublic
    Python
1

  2. LSTM-1DCNN-GRU-for-DepressionLSTM-1DCNN-GRU-for-DepressionPublic
    Jupyter Notebook
16

4

  3. EEG_Speech_Depression_MultiDLEEG_Speech_Depression_MultiDLPublic
    Jupyter Notebook
39

7

  4. ABIDE_Classification_DLModelABIDE_Classification_DLModelPublic
    Python
6

3

  5. FLARE_21_Segmentation_DLFLARE_21_Segmentation_DLPublic
    Python
4

  6. EEG-using-deep-LearningEEG-using-deep-LearningPublic
    
In this Basic Tutorial, I have used 1DCNN for EEG classification using random dataset, You can use your own dataset

    Jupyter Notebook
19

1


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
View RespectKnowledge's full-sized avatar
28
followers
 · 0
following

Achievements
Achievement: StarstruckAchievement: Arctic Code Vault Contributor
Achievements
Achievement: StarstruckAchievement: Arctic Code Vault Contributor

Block or report RespectKnowledge

Block user

Prevent this user from interacting with your repositories and sending you notifications.
Learn more about blocking users.


You must be logged in to block users.


Content in all repositories owned by your account will be closed.


Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.

Report abuse

Contact GitHub support about this user’s behavior.
Learn more about reporting abuse.

Report abuse
RespectKnowledge/README.md
🔬 I’m currently working at the intersection of Deep Learning and Medical Imaging, developing advanced models for segmentation, classification, and beyond.
🧠 My focus includes self-supervised, semi-supervised, and generative learning techniques to tackle real-world challenges where annotated data is scarce or noisy.
🏗️ I’m also exploring the design and training of foundation models for scalable and generalizable medical image understanding across modalities and tasks.
🤝 I'm keen to collaborate on innovative research and translational projects, particularly those with industry relevance and clinical impact.
🏆 MICCAI 2024 Challenge Contributions
🏅 PositionChallenge NameTitle/Method
🥇 1stAIMS-TBI - Automated Identification of Moderate-Severe Traumatic Brain Injury LesionsLeveraging Student-Teacher Networks in Self-Supervised Learning for Enhanced TBI Severity Segmentation
4thISLES - Ischemic Stroke Lesion SegmentationA Two-Stage SSL Approach for Ischemic Stroke Lesion Segmentation
🥈 2ndUWF4DR - Ultra-Widefield Fundus Imaging for Diabetic RetinopathyEfficient Deep Learning for Ultra-Widefield Fundus Imaging
🥈 2ndFETA - Fetal Tissue AnnotationPseudo Labeling + 3D Deep Learning Models
🥉 3rdMBH-Seg - Multi-class Brain Hemorrhage SegmentationEfficient SSL-Based Deep Learning for Hemorrhage Segmentation
🥉 3rdCARE - Real World Medical Image AnalysisTwo-Stage SSL for Whole Heart Segmentation in CT and MRI
4thCURVAS - Calibration and Uncertainty for Multi-Rater Volume AssessmentxSLTM-UNet Deep Learning Model
4thTriALS24 - Triphasic-Aided Liver Lesion SegmentationSSL-Based Student-Teacher Architecture for Non-Contrast CT
5thHNTSMRG - Head and Neck Tumor Segmentation in MRSelf-Supervised xLSTM-UNet for Tumor Segmentation
5thMBAS - Multi-class Bi-Atrial SegmentationStudent-Teacher SSL for 3D Bi-Atrial Segmentation
8thMARIO - AMD Progression in OCTEfficient DL Models for Age-Related Macular Degeneration Tracking
9thCOSAS - Cross-Organ and Scanner Adenocarcinoma SegmentationSwin-UNet + Parallel Cross-Attention
9thTopCoW - Anatomical Segmentation of the Circle of WillisPretrained 3D Segmentation Models
12thAortaSeg24 - Aortic Branch and Zone SegmentationSSL-Based Aortic Segmentation with Student-Teacher Architectures
🧠 IEEE ISBI 2023 Challenge Contributions
🏅 PositionChallenge Name
🥇 1stCuRIOUS 2022 - Image Registration & Segmentation
4thCMRxMotion Challenge
4thcSeg-2022 - Infant Cerebellum MRI Segmentation
6thATM’22 - Multi-Site Multi-Domain Airway Tree Modeling
9thNCCT - Intracranial Hemorrhage Segmentation
10thISLES'22 - Ischemic Stroke Lesion Segmentation
11thKidney Parsing Challenge - Renal Cancer Treatment
16thPulmonary Artery Segmentation Challenge
🔹 MICCAI 2022 Challenge Contributions
🏅 PositionChallenge NameMethod Summary
🥇 1stCuRIOUS – Correction of Brain Shift with Intra-Operative UltrasoundSelf-Supervised Two-Stage 3D ResUNet
4thCMRxMotion Challenge–
4thcSeg-2022 – Infant Cerebellum MRI Segmentation–
6thATM’22 – Multi-site, Multi-Domain Airway Tree Modeling3D Deep Learning Models
9thNCCT – Intracranial Hemorrhage Segmentation–
10thISLES'22 – Ischemic Stroke Lesion Segmentation–
11thKidney Parsing Challenge – Renal Cancer Treatment–
16thPulmonary Artery Segmentation Challenge–
🧠 MICCAI 2021 Challenge Contributions
🏅 PositionChallenge Name
4thDiabetic Foot Ulcer Challenge
4thFetReg - Placental Vessel Segmentation in Fetoscopy
5thFoot Ulcer Segmentation Challenge
7thFLARE - Fast and Low GPU Abdominal Organ Segmentation
10thRight Ventricular Segmentation in Cardiac MRI
13thFeta2021 - Fetal Brain Tissue Segmentation
6thChest XR COVID-19 Detection (Grand Challenge)
13thAIROGS - Robust Glaucoma Screening
5thKNIGHT Challenge - Kidney Clinical Notes & Imaging Biomarker Discovery
14thHECKTOR - Head and Neck Tumor PET/CT Segmentation

Pinned
Loading
  1. EMIDEC-ChallengeEMIDEC-ChallengePublic
    Python
1

  2. LSTM-1DCNN-GRU-for-DepressionLSTM-1DCNN-GRU-for-DepressionPublic
    Jupyter Notebook
16

4

  3. EEG_Speech_Depression_MultiDLEEG_Speech_Depression_MultiDLPublic
    Jupyter Notebook
39

7

  4. ABIDE_Classification_DLModelABIDE_Classification_DLModelPublic
    Python
6

3

  5. FLARE_21_Segmentation_DLFLARE_21_Segmentation_DLPublic
    Python
4

  6. EEG-using-deep-LearningEEG-using-deep-LearningPublic
    
In this Basic Tutorial, I have used 1DCNN for EEG classification using random dataset, You can use your own dataset

    Jupyter Notebook
19

1


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
View RespectKnowledge's full-sized avatar
28
followers
 · 0
following

Achievements
Achievement: StarstruckAchievement: Arctic Code Vault Contributor
Achievements
Achievement: StarstruckAchievement: Arctic Code Vault Contributor

Block or report RespectKnowledge

Block user

Prevent this user from interacting with your repositories and sending you notifications.
Learn more about blocking users.


You must be logged in to block users.


Content in all repositories owned by your account will be closed.


Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.

Report abuse

Contact GitHub support about this user’s behavior.
Learn more about reporting abuse.

Report abuse
RespectKnowledge/README.md
🔬 I’m currently working at the intersection of Deep Learning and Medical Imaging, developing advanced models for segmentation, classification, and beyond.
🧠 My focus includes self-supervised, semi-supervised, and generative learning techniques to tackle real-world challenges where annotated data is scarce or noisy.
🏗️ I’m also exploring the design and training of foundation models for scalable and generalizable medical image understanding across modalities and tasks.
🤝 I'm keen to collaborate on innovative research and translational projects, particularly those with industry relevance and clinical impact.
🏆 MICCAI 2024 Challenge Contributions
🏅 PositionChallenge NameTitle/Method
🥇 1stAIMS-TBI - Automated Identification of Moderate-Severe Traumatic Brain Injury LesionsLeveraging Student-Teacher Networks in Self-Supervised Learning for Enhanced TBI Severity Segmentation
4thISLES - Ischemic Stroke Lesion SegmentationA Two-Stage SSL Approach for Ischemic Stroke Lesion Segmentation
🥈 2ndUWF4DR - Ultra-Widefield Fundus Imaging for Diabetic RetinopathyEfficient Deep Learning for Ultra-Widefield Fundus Imaging
🥈 2ndFETA - Fetal Tissue AnnotationPseudo Labeling + 3D Deep Learning Models
🥉 3rdMBH-Seg - Multi-class Brain Hemorrhage SegmentationEfficient SSL-Based Deep Learning for Hemorrhage Segmentation
🥉 3rdCARE - Real World Medical Image AnalysisTwo-Stage SSL for Whole Heart Segmentation in CT and MRI
4thCURVAS - Calibration and Uncertainty for Multi-Rater Volume AssessmentxSLTM-UNet Deep Learning Model
4thTriALS24 - Triphasic-Aided Liver Lesion SegmentationSSL-Based Student-Teacher Architecture for Non-Contrast CT
5thHNTSMRG - Head and Neck Tumor Segmentation in MRSelf-Supervised xLSTM-UNet for Tumor Segmentation
5thMBAS - Multi-class Bi-Atrial SegmentationStudent-Teacher SSL for 3D Bi-Atrial Segmentation
8thMARIO - AMD Progression in OCTEfficient DL Models for Age-Related Macular Degeneration Tracking
9thCOSAS - Cross-Organ and Scanner Adenocarcinoma SegmentationSwin-UNet + Parallel Cross-Attention
9thTopCoW - Anatomical Segmentation of the Circle of WillisPretrained 3D Segmentation Models
12thAortaSeg24 - Aortic Branch and Zone SegmentationSSL-Based Aortic Segmentation with Student-Teacher Architectures
🧠 IEEE ISBI 2023 Challenge Contributions
🏅 PositionChallenge Name
🥇 1stCuRIOUS 2022 - Image Registration & Segmentation
4thCMRxMotion Challenge
4thcSeg-2022 - Infant Cerebellum MRI Segmentation
6thATM’22 - Multi-Site Multi-Domain Airway Tree Modeling
9thNCCT - Intracranial Hemorrhage Segmentation
10thISLES'22 - Ischemic Stroke Lesion Segmentation
11thKidney Parsing Challenge - Renal Cancer Treatment
16thPulmonary Artery Segmentation Challenge
🔹 MICCAI 2022 Challenge Contributions
🏅 PositionChallenge NameMethod Summary
🥇 1stCuRIOUS – Correction of Brain Shift with Intra-Operative UltrasoundSelf-Supervised Two-Stage 3D ResUNet
4thCMRxMotion Challenge–
4thcSeg-2022 – Infant Cerebellum MRI Segmentation–
6thATM’22 – Multi-site, Multi-Domain Airway Tree Modeling3D Deep Learning Models
9thNCCT – Intracranial Hemorrhage Segmentation–
10thISLES'22 – Ischemic Stroke Lesion Segmentation–
11thKidney Parsing Challenge – Renal Cancer Treatment–
16thPulmonary Artery Segmentation Challenge–
🧠 MICCAI 2021 Challenge Contributions
🏅 PositionChallenge Name
4thDiabetic Foot Ulcer Challenge
4thFetReg - Placental Vessel Segmentation in Fetoscopy
5thFoot Ulcer Segmentation Challenge
7thFLARE - Fast and Low GPU Abdominal Organ Segmentation
10thRight Ventricular Segmentation in Cardiac MRI
13thFeta2021 - Fetal Brain Tissue Segmentation
6thChest XR COVID-19 Detection (Grand Challenge)
13thAIROGS - Robust Glaucoma Screening
5thKNIGHT Challenge - Kidney Clinical Notes & Imaging Biomarker Discovery
14thHECKTOR - Head and Neck Tumor PET/CT Segmentation

Pinned
Loading
  1. EMIDEC-ChallengeEMIDEC-ChallengePublic
    Python
1

  2. LSTM-1DCNN-GRU-for-DepressionLSTM-1DCNN-GRU-for-DepressionPublic
    Jupyter Notebook
16

4

  3. EEG_Speech_Depression_MultiDLEEG_Speech_Depression_MultiDLPublic
    Jupyter Notebook
39

7

  4. ABIDE_Classification_DLModelABIDE_Classification_DLModelPublic
    Python
6

3

  5. FLARE_21_Segmentation_DLFLARE_21_Segmentation_DLPublic
    Python
4

  6. EEG-using-deep-LearningEEG-using-deep-LearningPublic
    
In this Basic Tutorial, I have used 1DCNN for EEG classification using random dataset, You can use your own dataset

    Jupyter Notebook
19

1


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
View RespectKnowledge's full-sized avatar
28
followers
 · 0
following

Achievements
Achievement: StarstruckAchievement: Arctic Code Vault Contributor
Achievements
Achievement: StarstruckAchievement: Arctic Code Vault Contributor

Block or report RespectKnowledge

Block user

Prevent this user from interacting with your repositories and sending you notifications.
Learn more about blocking users.


You must be logged in to block users.


Content in all repositories owned by your account will be closed.


Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.

Report abuse

Contact GitHub support about this user’s behavior.
Learn more about reporting abuse.

Report abuse
RespectKnowledge/README.md
🔬 I’m currently working at the intersection of Deep Learning and Medical Imaging, developing advanced models for segmentation, classification, and beyond.
🧠 My focus includes self-supervised, semi-supervised, and generative learning techniques to tackle real-world challenges where annotated data is scarce or noisy.
🏗️ I’m also exploring the design and training of foundation models for scalable and generalizable medical image understanding across modalities and tasks.
🤝 I'm keen to collaborate on innovative research and translational projects, particularly those with industry relevance and clinical impact.
🏆 MICCAI 2024 Challenge Contributions
🏅 PositionChallenge NameTitle/Method
🥇 1stAIMS-TBI - Automated Identification of Moderate-Severe Traumatic Brain Injury LesionsLeveraging Student-Teacher Networks in Self-Supervised Learning for Enhanced TBI Severity Segmentation
4thISLES - Ischemic Stroke Lesion SegmentationA Two-Stage SSL Approach for Ischemic Stroke Lesion Segmentation
🥈 2ndUWF4DR - Ultra-Widefield Fundus Imaging for Diabetic RetinopathyEfficient Deep Learning for Ultra-Widefield Fundus Imaging
🥈 2ndFETA - Fetal Tissue AnnotationPseudo Labeling + 3D Deep Learning Models
🥉 3rdMBH-Seg - Multi-class Brain Hemorrhage SegmentationEfficient SSL-Based Deep Learning for Hemorrhage Segmentation
🥉 3rdCARE - Real World Medical Image AnalysisTwo-Stage SSL for Whole Heart Segmentation in CT and MRI
4thCURVAS - Calibration and Uncertainty for Multi-Rater Volume AssessmentxSLTM-UNet Deep Learning Model
4thTriALS24 - Triphasic-Aided Liver Lesion SegmentationSSL-Based Student-Teacher Architecture for Non-Contrast CT
5thHNTSMRG - Head and Neck Tumor Segmentation in MRSelf-Supervised xLSTM-UNet for Tumor Segmentation
5thMBAS - Multi-class Bi-Atrial SegmentationStudent-Teacher SSL for 3D Bi-Atrial Segmentation
8thMARIO - AMD Progression in OCTEfficient DL Models for Age-Related Macular Degeneration Tracking
9thCOSAS - Cross-Organ and Scanner Adenocarcinoma SegmentationSwin-UNet + Parallel Cross-Attention
9thTopCoW - Anatomical Segmentation of the Circle of WillisPretrained 3D Segmentation Models
12thAortaSeg24 - Aortic Branch and Zone SegmentationSSL-Based Aortic Segmentation with Student-Teacher Architectures
🧠 IEEE ISBI 2023 Challenge Contributions
🏅 PositionChallenge Name
🥇 1stCuRIOUS 2022 - Image Registration & Segmentation
4thCMRxMotion Challenge
4thcSeg-2022 - Infant Cerebellum MRI Segmentation
6thATM’22 - Multi-Site Multi-Domain Airway Tree Modeling
9thNCCT - Intracranial Hemorrhage Segmentation
10thISLES'22 - Ischemic Stroke Lesion Segmentation
11thKidney Parsing Challenge - Renal Cancer Treatment
16thPulmonary Artery Segmentation Challenge
🔹 MICCAI 2022 Challenge Contributions
🏅 PositionChallenge NameMethod Summary
🥇 1stCuRIOUS – Correction of Brain Shift with Intra-Operative UltrasoundSelf-Supervised Two-Stage 3D ResUNet
4thCMRxMotion Challenge–
4thcSeg-2022 – Infant Cerebellum MRI Segmentation–
6thATM’22 – Multi-site, Multi-Domain Airway Tree Modeling3D Deep Learning Models
9thNCCT – Intracranial Hemorrhage Segmentation–
10thISLES'22 – Ischemic Stroke Lesion Segmentation–
11thKidney Parsing Challenge – Renal Cancer Treatment–
16thPulmonary Artery Segmentation Challenge–
🧠 MICCAI 2021 Challenge Contributions
🏅 PositionChallenge Name
4thDiabetic Foot Ulcer Challenge
4thFetReg - Placental Vessel Segmentation in Fetoscopy
5thFoot Ulcer Segmentation Challenge
7thFLARE - Fast and Low GPU Abdominal Organ Segmentation
10thRight Ventricular Segmentation in Cardiac MRI
13thFeta2021 - Fetal Brain Tissue Segmentation
6thChest XR COVID-19 Detection (Grand Challenge)
13thAIROGS - Robust Glaucoma Screening
5thKNIGHT Challenge - Kidney Clinical Notes & Imaging Biomarker Discovery
14thHECKTOR - Head and Neck Tumor PET/CT Segmentation

Pinned
Loading
  1. EMIDEC-ChallengeEMIDEC-ChallengePublic
    Python
1

  2. LSTM-1DCNN-GRU-for-DepressionLSTM-1DCNN-GRU-for-DepressionPublic
    Jupyter Notebook
16

4

  3. EEG_Speech_Depression_MultiDLEEG_Speech_Depression_MultiDLPublic
    Jupyter Notebook
39

7

  4. ABIDE_Classification_DLModelABIDE_Classification_DLModelPublic
    Python
6

3

  5. FLARE_21_Segmentation_DLFLARE_21_Segmentation_DLPublic
    Python
4

  6. EEG-using-deep-LearningEEG-using-deep-LearningPublic
    
In this Basic Tutorial, I have used 1DCNN for EEG classification using random dataset, You can use your own dataset

    Jupyter Notebook
19

1


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
View RespectKnowledge's full-sized avatar
28
followers
 · 0
following

Achievements
Achievement: StarstruckAchievement: Arctic Code Vault Contributor
Achievements
Achievement: StarstruckAchievement: Arctic Code Vault Contributor

Block or report RespectKnowledge

Block user

Prevent this user from interacting with your repositories and sending you notifications.
Learn more about blocking users.


You must be logged in to block users.


Content in all repositories owned by your account will be closed.


Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.

Report abuse

Contact GitHub support about this user’s behavior.
Learn more about reporting abuse.

Report abuse
RespectKnowledge/README.md
🔬 I’m currently working at the intersection of Deep Learning and Medical Imaging, developing advanced models for segmentation, classification, and beyond.
🧠 My focus includes self-supervised, semi-supervised, and generative learning techniques to tackle real-world challenges where annotated data is scarce or noisy.
🏗️ I’m also exploring the design and training of foundation models for scalable and generalizable medical image understanding across modalities and tasks.
🤝 I'm keen to collaborate on innovative research and translational projects, particularly those with industry relevance and clinical impact.
🏆 MICCAI 2024 Challenge Contributions
🏅 PositionChallenge NameTitle/Method
🥇 1stAIMS-TBI - Automated Identification of Moderate-Severe Traumatic Brain Injury LesionsLeveraging Student-Teacher Networks in Self-Supervised Learning for Enhanced TBI Severity Segmentation
4thISLES - Ischemic Stroke Lesion SegmentationA Two-Stage SSL Approach for Ischemic Stroke Lesion Segmentation
🥈 2ndUWF4DR - Ultra-Widefield Fundus Imaging for Diabetic RetinopathyEfficient Deep Learning for Ultra-Widefield Fundus Imaging
🥈 2ndFETA - Fetal Tissue AnnotationPseudo Labeling + 3D Deep Learning Models
🥉 3rdMBH-Seg - Multi-class Brain Hemorrhage SegmentationEfficient SSL-Based Deep Learning for Hemorrhage Segmentation
🥉 3rdCARE - Real World Medical Image AnalysisTwo-Stage SSL for Whole Heart Segmentation in CT and MRI
4thCURVAS - Calibration and Uncertainty for Multi-Rater Volume AssessmentxSLTM-UNet Deep Learning Model
4thTriALS24 - Triphasic-Aided Liver Lesion SegmentationSSL-Based Student-Teacher Architecture for Non-Contrast CT
5thHNTSMRG - Head and Neck Tumor Segmentation in MRSelf-Supervised xLSTM-UNet for Tumor Segmentation
5thMBAS - Multi-class Bi-Atrial SegmentationStudent-Teacher SSL for 3D Bi-Atrial Segmentation
8thMARIO - AMD Progression in OCTEfficient DL Models for Age-Related Macular Degeneration Tracking
9thCOSAS - Cross-Organ and Scanner Adenocarcinoma SegmentationSwin-UNet + Parallel Cross-Attention
9thTopCoW - Anatomical Segmentation of the Circle of WillisPretrained 3D Segmentation Models
12thAortaSeg24 - Aortic Branch and Zone SegmentationSSL-Based Aortic Segmentation with Student-Teacher Architectures
🧠 IEEE ISBI 2023 Challenge Contributions
🏅 PositionChallenge Name
🥇 1stCuRIOUS 2022 - Image Registration & Segmentation
4thCMRxMotion Challenge
4thcSeg-2022 - Infant Cerebellum MRI Segmentation
6thATM’22 - Multi-Site Multi-Domain Airway Tree Modeling
9thNCCT - Intracranial Hemorrhage Segmentation
10thISLES'22 - Ischemic Stroke Lesion Segmentation
11thKidney Parsing Challenge - Renal Cancer Treatment
16thPulmonary Artery Segmentation Challenge
🔹 MICCAI 2022 Challenge Contributions
🏅 PositionChallenge NameMethod Summary
🥇 1stCuRIOUS – Correction of Brain Shift with Intra-Operative UltrasoundSelf-Supervised Two-Stage 3D ResUNet
4thCMRxMotion Challenge–
4thcSeg-2022 – Infant Cerebellum MRI Segmentation–
6thATM’22 – Multi-site, Multi-Domain Airway Tree Modeling3D Deep Learning Models
9thNCCT – Intracranial Hemorrhage Segmentation–
10thISLES'22 – Ischemic Stroke Lesion Segmentation–
11thKidney Parsing Challenge – Renal Cancer Treatment–
16thPulmonary Artery Segmentation Challenge–
🧠 MICCAI 2021 Challenge Contributions
🏅 PositionChallenge Name
4thDiabetic Foot Ulcer Challenge
4thFetReg - Placental Vessel Segmentation in Fetoscopy
5thFoot Ulcer Segmentation Challenge
7thFLARE - Fast and Low GPU Abdominal Organ Segmentation
10thRight Ventricular Segmentation in Cardiac MRI
13thFeta2021 - Fetal Brain Tissue Segmentation
6thChest XR COVID-19 Detection (Grand Challenge)
13thAIROGS - Robust Glaucoma Screening
5thKNIGHT Challenge - Kidney Clinical Notes & Imaging Biomarker Discovery
14thHECKTOR - Head and Neck Tumor PET/CT Segmentation

Pinned
Loading
  1. EMIDEC-ChallengeEMIDEC-ChallengePublic
    Python
1

  2. LSTM-1DCNN-GRU-for-DepressionLSTM-1DCNN-GRU-for-DepressionPublic
    Jupyter Notebook
16

4

  3. EEG_Speech_Depression_MultiDLEEG_Speech_Depression_MultiDLPublic
    Jupyter Notebook
39

7

  4. ABIDE_Classification_DLModelABIDE_Classification_DLModelPublic
    Python
6

3

  5. FLARE_21_Segmentation_DLFLARE_21_Segmentation_DLPublic
    Python
4

  6. EEG-using-deep-LearningEEG-using-deep-LearningPublic
    
In this Basic Tutorial, I have used 1DCNN for EEG classification using random dataset, You can use your own dataset

    Jupyter Notebook
19

1


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
View RespectKnowledge's full-sized avatar
28
followers
 · 0
following

Achievements
Achievement: StarstruckAchievement: Arctic Code Vault Contributor
Achievements
Achievement: StarstruckAchievement: Arctic Code Vault Contributor

Block or report RespectKnowledge

Block user

Prevent this user from interacting with your repositories and sending you notifications.
Learn more about blocking users.


You must be logged in to block users.


Content in all repositories owned by your account will be closed.


Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.

Report abuse

Contact GitHub support about this user’s behavior.
Learn more about reporting abuse.

Report abuse
RespectKnowledge/README.md
🔬 I’m currently working at the intersection of Deep Learning and Medical Imaging, developing advanced models for segmentation, classification, and beyond.
🧠 My focus includes self-supervised, semi-supervised, and generative learning techniques to tackle real-world challenges where annotated data is scarce or noisy.
🏗️ I’m also exploring the design and training of foundation models for scalable and generalizable medical image understanding across modalities and tasks.
🤝 I'm keen to collaborate on innovative research and translational projects, particularly those with industry relevance and clinical impact.
🏆 MICCAI 2024 Challenge Contributions
🏅 PositionChallenge NameTitle/Method
🥇 1stAIMS-TBI - Automated Identification of Moderate-Severe Traumatic Brain Injury LesionsLeveraging Student-Teacher Networks in Self-Supervised Learning for Enhanced TBI Severity Segmentation
4thISLES - Ischemic Stroke Lesion SegmentationA Two-Stage SSL Approach for Ischemic Stroke Lesion Segmentation
🥈 2ndUWF4DR - Ultra-Widefield Fundus Imaging for Diabetic RetinopathyEfficient Deep Learning for Ultra-Widefield Fundus Imaging
🥈 2ndFETA - Fetal Tissue AnnotationPseudo Labeling + 3D Deep Learning Models
🥉 3rdMBH-Seg - Multi-class Brain Hemorrhage SegmentationEfficient SSL-Based Deep Learning for Hemorrhage Segmentation
🥉 3rdCARE - Real World Medical Image AnalysisTwo-Stage SSL for Whole Heart Segmentation in CT and MRI
4thCURVAS - Calibration and Uncertainty for Multi-Rater Volume AssessmentxSLTM-UNet Deep Learning Model
4thTriALS24 - Triphasic-Aided Liver Lesion SegmentationSSL-Based Student-Teacher Architecture for Non-Contrast CT
5thHNTSMRG - Head and Neck Tumor Segmentation in MRSelf-Supervised xLSTM-UNet for Tumor Segmentation
5thMBAS - Multi-class Bi-Atrial SegmentationStudent-Teacher SSL for 3D Bi-Atrial Segmentation
8thMARIO - AMD Progression in OCTEfficient DL Models for Age-Related Macular Degeneration Tracking
9thCOSAS - Cross-Organ and Scanner Adenocarcinoma SegmentationSwin-UNet + Parallel Cross-Attention
9thTopCoW - Anatomical Segmentation of the Circle of WillisPretrained 3D Segmentation Models
12thAortaSeg24 - Aortic Branch and Zone SegmentationSSL-Based Aortic Segmentation with Student-Teacher Architectures
🧠 IEEE ISBI 2023 Challenge Contributions
🏅 PositionChallenge Name
🥇 1stCuRIOUS 2022 - Image Registration & Segmentation
4thCMRxMotion Challenge
4thcSeg-2022 - Infant Cerebellum MRI Segmentation
6thATM’22 - Multi-Site Multi-Domain Airway Tree Modeling
9thNCCT - Intracranial Hemorrhage Segmentation
10thISLES'22 - Ischemic Stroke Lesion Segmentation
11thKidney Parsing Challenge - Renal Cancer Treatment
16thPulmonary Artery Segmentation Challenge
🔹 MICCAI 2022 Challenge Contributions
🏅 PositionChallenge NameMethod Summary
🥇 1stCuRIOUS – Correction of Brain Shift with Intra-Operative UltrasoundSelf-Supervised Two-Stage 3D ResUNet
4thCMRxMotion Challenge–
4thcSeg-2022 – Infant Cerebellum MRI Segmentation–
6thATM’22 – Multi-site, Multi-Domain Airway Tree Modeling3D Deep Learning Models
9thNCCT – Intracranial Hemorrhage Segmentation–
10thISLES'22 – Ischemic Stroke Lesion Segmentation–
11thKidney Parsing Challenge – Renal Cancer Treatment–
16thPulmonary Artery Segmentation Challenge–
🧠 MICCAI 2021 Challenge Contributions
🏅 PositionChallenge Name
4thDiabetic Foot Ulcer Challenge
4thFetReg - Placental Vessel Segmentation in Fetoscopy
5thFoot Ulcer Segmentation Challenge
7thFLARE - Fast and Low GPU Abdominal Organ Segmentation
10thRight Ventricular Segmentation in Cardiac MRI
13thFeta2021 - Fetal Brain Tissue Segmentation
6thChest XR COVID-19 Detection (Grand Challenge)
13thAIROGS - Robust Glaucoma Screening
5thKNIGHT Challenge - Kidney Clinical Notes & Imaging Biomarker Discovery
14thHECKTOR - Head and Neck Tumor PET/CT Segmentation

Pinned
Loading
  1. EMIDEC-ChallengeEMIDEC-ChallengePublic
    Python
1

  2. LSTM-1DCNN-GRU-for-DepressionLSTM-1DCNN-GRU-for-DepressionPublic
    Jupyter Notebook
16

4

  3. EEG_Speech_Depression_MultiDLEEG_Speech_Depression_MultiDLPublic
    Jupyter Notebook
39

7

  4. ABIDE_Classification_DLModelABIDE_Classification_DLModelPublic
    Python
6

3

  5. FLARE_21_Segmentation_DLFLARE_21_Segmentation_DLPublic
    Python
4

  6. EEG-using-deep-LearningEEG-using-deep-LearningPublic
    
In this Basic Tutorial, I have used 1DCNN for EEG classification using random dataset, You can use your own dataset

    Jupyter Notebook
19

1


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
View RespectKnowledge's full-sized avatar
28
followers
 · 0
following

Achievements
Achievement: StarstruckAchievement: Arctic Code Vault Contributor
Achievements
Achievement: StarstruckAchievement: Arctic Code Vault Contributor

Block or report RespectKnowledge

Block user

Prevent this user from interacting with your repositories and sending you notifications.
Learn more about blocking users.


You must be logged in to block users.


Content in all repositories owned by your account will be closed.


Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.

Report abuse

Contact GitHub support about this user’s behavior.
Learn more about reporting abuse.

Report abuse
RespectKnowledge/README.md
🔬 I’m currently working at the intersection of Deep Learning and Medical Imaging, developing advanced models for segmentation, classification, and beyond.
🧠 My focus includes self-supervised, semi-supervised, and generative learning techniques to tackle real-world challenges where annotated data is scarce or noisy.
🏗️ I’m also exploring the design and training of foundation models for scalable and generalizable medical image understanding across modalities and tasks.
🤝 I'm keen to collaborate on innovative research and translational projects, particularly those with industry relevance and clinical impact.
🏆 MICCAI 2024 Challenge Contributions
🏅 PositionChallenge NameTitle/Method
🥇 1stAIMS-TBI - Automated Identification of Moderate-Severe Traumatic Brain Injury LesionsLeveraging Student-Teacher Networks in Self-Supervised Learning for Enhanced TBI Severity Segmentation
4thISLES - Ischemic Stroke Lesion SegmentationA Two-Stage SSL Approach for Ischemic Stroke Lesion Segmentation
🥈 2ndUWF4DR - Ultra-Widefield Fundus Imaging for Diabetic RetinopathyEfficient Deep Learning for Ultra-Widefield Fundus Imaging
🥈 2ndFETA - Fetal Tissue AnnotationPseudo Labeling + 3D Deep Learning Models
🥉 3rdMBH-Seg - Multi-class Brain Hemorrhage SegmentationEfficient SSL-Based Deep Learning for Hemorrhage Segmentation
🥉 3rdCARE - Real World Medical Image AnalysisTwo-Stage SSL for Whole Heart Segmentation in CT and MRI
4thCURVAS - Calibration and Uncertainty for Multi-Rater Volume AssessmentxSLTM-UNet Deep Learning Model
4thTriALS24 - Triphasic-Aided Liver Lesion SegmentationSSL-Based Student-Teacher Architecture for Non-Contrast CT
5thHNTSMRG - Head and Neck Tumor Segmentation in MRSelf-Supervised xLSTM-UNet for Tumor Segmentation
5thMBAS - Multi-class Bi-Atrial SegmentationStudent-Teacher SSL for 3D Bi-Atrial Segmentation
8thMARIO - AMD Progression in OCTEfficient DL Models for Age-Related Macular Degeneration Tracking
9thCOSAS - Cross-Organ and Scanner Adenocarcinoma SegmentationSwin-UNet + Parallel Cross-Attention
9thTopCoW - Anatomical Segmentation of the Circle of WillisPretrained 3D Segmentation Models
12thAortaSeg24 - Aortic Branch and Zone SegmentationSSL-Based Aortic Segmentation with Student-Teacher Architectures
🧠 IEEE ISBI 2023 Challenge Contributions
🏅 PositionChallenge Name
🥇 1stCuRIOUS 2022 - Image Registration & Segmentation
4thCMRxMotion Challenge
4thcSeg-2022 - Infant Cerebellum MRI Segmentation
6thATM’22 - Multi-Site Multi-Domain Airway Tree Modeling
9thNCCT - Intracranial Hemorrhage Segmentation
10thISLES'22 - Ischemic Stroke Lesion Segmentation
11thKidney Parsing Challenge - Renal Cancer Treatment
16thPulmonary Artery Segmentation Challenge
🔹 MICCAI 2022 Challenge Contributions
🏅 PositionChallenge NameMethod Summary
🥇 1stCuRIOUS – Correction of Brain Shift with Intra-Operative UltrasoundSelf-Supervised Two-Stage 3D ResUNet
4thCMRxMotion Challenge–
4thcSeg-2022 – Infant Cerebellum MRI Segmentation–
6thATM’22 – Multi-site, Multi-Domain Airway Tree Modeling3D Deep Learning Models
9thNCCT – Intracranial Hemorrhage Segmentation–
10thISLES'22 – Ischemic Stroke Lesion Segmentation–
11thKidney Parsing Challenge – Renal Cancer Treatment–
16thPulmonary Artery Segmentation Challenge–
🧠 MICCAI 2021 Challenge Contributions
🏅 PositionChallenge Name
4thDiabetic Foot Ulcer Challenge
4thFetReg - Placental Vessel Segmentation in Fetoscopy
5thFoot Ulcer Segmentation Challenge
7thFLARE - Fast and Low GPU Abdominal Organ Segmentation
10thRight Ventricular Segmentation in Cardiac MRI
13thFeta2021 - Fetal Brain Tissue Segmentation
6thChest XR COVID-19 Detection (Grand Challenge)
13thAIROGS - Robust Glaucoma Screening
5thKNIGHT Challenge - Kidney Clinical Notes & Imaging Biomarker Discovery
14thHECKTOR - Head and Neck Tumor PET/CT Segmentation

Pinned
Loading
  1. EMIDEC-ChallengeEMIDEC-ChallengePublic
    Python
1

  2. LSTM-1DCNN-GRU-for-DepressionLSTM-1DCNN-GRU-for-DepressionPublic
    Jupyter Notebook
16

4

  3. EEG_Speech_Depression_MultiDLEEG_Speech_Depression_MultiDLPublic
    Jupyter Notebook
39

7

  4. ABIDE_Classification_DLModelABIDE_Classification_DLModelPublic
    Python
6

3

  5. FLARE_21_Segmentation_DLFLARE_21_Segmentation_DLPublic
    Python
4

  6. EEG-using-deep-LearningEEG-using-deep-LearningPublic
    
In this Basic Tutorial, I have used 1DCNN for EEG classification using random dataset, You can use your own dataset

    Jupyter Notebook
19

1