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AMIA Kaggle Challenge

Introduction

This repository contains our code for the AMIA Kaggle Challenge as part of the "Computer vision for biomedical images" seminar from Sören Lukassen @FU Berlin.
We implemented some preprocessing steps to analyse the images and their labels, before doing a cluster analysis of the bounding boxes to obtain adequate anchor boxes for training different object detection models. The goal of this project was predicting up to 14 different findings in chest X-ray images.
We trained FasterRCNN and RetinaNet with different hyperparameters to find the best model for the given dataset.

A full report on how we tackled this project can be found here (Max' report).
Alternatively, an HTML version of the project report can be found here (Flo's report).

About

segXray - Bounding box prediction for 14 different findings in chest X-ray images

floherzler.notion.site/AMIA-Kaggle-Challenge-2024-51cf2f6466b3486eb70e3928f64acf56#7d12f74f9f1446a2b4bb931e576fa4c3

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bounding-box-regressionfasterrcnnk-meansobject-detectionpreprocessingpytorchresnet-50retinanetx-ray

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, '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" + '

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AMIA Kaggle Challenge
Introduction
This repository contains our code for the AMIA Kaggle Challenge as part of the "Computer vision for biomedical images" seminar from Sören Lukassen @FU Berlin.

We implemented some preprocessing steps to analyse the images and their labels, before doing a cluster analysis of the bounding boxes to obtain adequate anchor boxes for training different object detection models.
The goal of this project was predicting up to 14 different findings in chest X-ray images.

We trained FasterRCNN and RetinaNet with different hyperparameters to find the best model for the given dataset.
A full report on how we tackled this project can be found here (Max' report).

Alternatively, an HTML version of the project report can be found here (Flo's report).
About
segXray - Bounding box prediction for 14 different findings in chest X-ray images
floherzler.notion.site/AMIA-Kaggle-Challenge-2024-51cf2f6466b3486eb70e3928f64acf56#7d12f74f9f1446a2b4bb931e576fa4c3
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bounding-box-regressionfasterrcnnk-meansobject-detectionpreprocessingpytorchresnet-50retinanetx-ray
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, '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('^' + ".*" + '

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AMIA Kaggle Challenge
Introduction
This repository contains our code for the AMIA Kaggle Challenge as part of the "Computer vision for biomedical images" seminar from Sören Lukassen @FU Berlin.

We implemented some preprocessing steps to analyse the images and their labels, before doing a cluster analysis of the bounding boxes to obtain adequate anchor boxes for training different object detection models.
The goal of this project was predicting up to 14 different findings in chest X-ray images.

We trained FasterRCNN and RetinaNet with different hyperparameters to find the best model for the given dataset.
A full report on how we tackled this project can be found here (Max' report).

Alternatively, an HTML version of the project report can be found here (Flo's report).
About
segXray - Bounding box prediction for 14 different findings in chest X-ray images
floherzler.notion.site/AMIA-Kaggle-Challenge-2024-51cf2f6466b3486eb70e3928f64acf56#7d12f74f9f1446a2b4bb931e576fa4c3
Topics
bounding-box-regressionfasterrcnnk-meansobject-detectionpreprocessingpytorchresnet-50retinanetx-ray
Resources
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, '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('^' + ".*" + '

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AMIA Kaggle Challenge
Introduction
This repository contains our code for the AMIA Kaggle Challenge as part of the "Computer vision for biomedical images" seminar from Sören Lukassen @FU Berlin.

We implemented some preprocessing steps to analyse the images and their labels, before doing a cluster analysis of the bounding boxes to obtain adequate anchor boxes for training different object detection models.
The goal of this project was predicting up to 14 different findings in chest X-ray images.

We trained FasterRCNN and RetinaNet with different hyperparameters to find the best model for the given dataset.
A full report on how we tackled this project can be found here (Max' report).

Alternatively, an HTML version of the project report can be found here (Flo's report).
About
segXray - Bounding box prediction for 14 different findings in chest X-ray images
floherzler.notion.site/AMIA-Kaggle-Challenge-2024-51cf2f6466b3486eb70e3928f64acf56#7d12f74f9f1446a2b4bb931e576fa4c3
Topics
bounding-box-regressionfasterrcnnk-meansobject-detectionpreprocessingpytorchresnet-50retinanetx-ray
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, '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" + '

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AMIA Kaggle Challenge
Introduction
This repository contains our code for the AMIA Kaggle Challenge as part of the "Computer vision for biomedical images" seminar from Sören Lukassen @FU Berlin.

We implemented some preprocessing steps to analyse the images and their labels, before doing a cluster analysis of the bounding boxes to obtain adequate anchor boxes for training different object detection models.
The goal of this project was predicting up to 14 different findings in chest X-ray images.

We trained FasterRCNN and RetinaNet with different hyperparameters to find the best model for the given dataset.
A full report on how we tackled this project can be found here (Max' report).

Alternatively, an HTML version of the project report can be found here (Flo's report).
About
segXray - Bounding box prediction for 14 different findings in chest X-ray images
floherzler.notion.site/AMIA-Kaggle-Challenge-2024-51cf2f6466b3486eb70e3928f64acf56#7d12f74f9f1446a2b4bb931e576fa4c3
Topics
bounding-box-regressionfasterrcnnk-meansobject-detectionpreprocessingpytorchresnet-50retinanetx-ray
Resources
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, '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('^' + ".*" + '

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AMIA Kaggle Challenge
Introduction
This repository contains our code for the AMIA Kaggle Challenge as part of the "Computer vision for biomedical images" seminar from Sören Lukassen @FU Berlin.

We implemented some preprocessing steps to analyse the images and their labels, before doing a cluster analysis of the bounding boxes to obtain adequate anchor boxes for training different object detection models.
The goal of this project was predicting up to 14 different findings in chest X-ray images.

We trained FasterRCNN and RetinaNet with different hyperparameters to find the best model for the given dataset.
A full report on how we tackled this project can be found here (Max' report).

Alternatively, an HTML version of the project report can be found here (Flo's report).
About
segXray - Bounding box prediction for 14 different findings in chest X-ray images
floherzler.notion.site/AMIA-Kaggle-Challenge-2024-51cf2f6466b3486eb70e3928f64acf56#7d12f74f9f1446a2b4bb931e576fa4c3
Topics
bounding-box-regressionfasterrcnnk-meansobject-detectionpreprocessingpytorchresnet-50retinanetx-ray
Resources
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, '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('^' + ".*" + '

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AMIA Kaggle Challenge
Introduction
This repository contains our code for the AMIA Kaggle Challenge as part of the "Computer vision for biomedical images" seminar from Sören Lukassen @FU Berlin.

We implemented some preprocessing steps to analyse the images and their labels, before doing a cluster analysis of the bounding boxes to obtain adequate anchor boxes for training different object detection models.
The goal of this project was predicting up to 14 different findings in chest X-ray images.

We trained FasterRCNN and RetinaNet with different hyperparameters to find the best model for the given dataset.
A full report on how we tackled this project can be found here (Max' report).

Alternatively, an HTML version of the project report can be found here (Flo's report).
About
segXray - Bounding box prediction for 14 different findings in chest X-ray images
floherzler.notion.site/AMIA-Kaggle-Challenge-2024-51cf2f6466b3486eb70e3928f64acf56#7d12f74f9f1446a2b4bb931e576fa4c3
Topics
bounding-box-regressionfasterrcnnk-meansobject-detectionpreprocessingpytorchresnet-50retinanetx-ray
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, '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); }
})();
})();

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AMIA Kaggle Challenge
Introduction
This repository contains our code for the AMIA Kaggle Challenge as part of the "Computer vision for biomedical images" seminar from Sören Lukassen @FU Berlin.

We implemented some preprocessing steps to analyse the images and their labels, before doing a cluster analysis of the bounding boxes to obtain adequate anchor boxes for training different object detection models.
The goal of this project was predicting up to 14 different findings in chest X-ray images.

We trained FasterRCNN and RetinaNet with different hyperparameters to find the best model for the given dataset.
A full report on how we tackled this project can be found here (Max' report).

Alternatively, an HTML version of the project report can be found here (Flo's report).
About
segXray - Bounding box prediction for 14 different findings in chest X-ray images
floherzler.notion.site/AMIA-Kaggle-Challenge-2024-51cf2f6466b3486eb70e3928f64acf56#7d12f74f9f1446a2b4bb931e576fa4c3
Topics
bounding-box-regressionfasterrcnnk-meansobject-detectionpreprocessingpytorchresnet-50retinanetx-ray
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Readme
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