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ABIDE_Classification_DLModels

Please download the ABIDE dataset from the following link. https://www.nitrc.org/ir/app/template/XDATScreen_report_xnat_projectData.vm/search_element/xnat:projectData/search_field/xnat:projectData.ID/search_value/ABIDE

Use data preprocess file to convert all data using different atlases and save into numpy for each atlas

You can get the different atlases from the following link

https://nilearn.github.io/modules/generated/nilearn.plotting.plot_prob_atlas.html

https://github.com/Parietal-INRIA/DiFuMo

Use data generator to convert data into batches

train, test and optimize the deep learning models.

We will update more results and evaluation code after accepting of the paper.

All codes are written in python and all deep learning models are written in PyTorch library. https://pytorch.org/

https://numpy.org/doc/stable/reference/index.html

If code will be useful, Please cite this paper

https://link.springer.com/chapter/10.1007/978-981-16-7167-8_77

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} 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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ABIDE_Classification_DLModels
Please download the ABIDE dataset from the following link.
https://www.nitrc.org/ir/app/template/XDATScreen_report_xnat_projectData.vm/search_element/xnat:projectData/search_field/xnat:projectData.ID/search_value/ABIDE
Use data preprocess file to convert all data using different atlases and save into numpy for each atlas
You can get the different atlases from the following link
https://nilearn.github.io/modules/generated/nilearn.plotting.plot_prob_atlas.html
https://github.com/Parietal-INRIA/DiFuMo
Use data generator to convert data into batches
train, test and optimize the deep learning models.
We will update more results and evaluation code after accepting of the paper.
All codes are written in python and all deep learning models are written in PyTorch library.
https://pytorch.org/
https://numpy.org/doc/stable/reference/index.html
If code will be useful, Please cite this paper
https://link.springer.com/chapter/10.1007/978-981-16-7167-8_77
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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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ABIDE_Classification_DLModels
Please download the ABIDE dataset from the following link.
https://www.nitrc.org/ir/app/template/XDATScreen_report_xnat_projectData.vm/search_element/xnat:projectData/search_field/xnat:projectData.ID/search_value/ABIDE
Use data preprocess file to convert all data using different atlases and save into numpy for each atlas
You can get the different atlases from the following link
https://nilearn.github.io/modules/generated/nilearn.plotting.plot_prob_atlas.html
https://github.com/Parietal-INRIA/DiFuMo
Use data generator to convert data into batches
train, test and optimize the deep learning models.
We will update more results and evaluation code after accepting of the paper.
All codes are written in python and all deep learning models are written in PyTorch library.
https://pytorch.org/
https://numpy.org/doc/stable/reference/index.html
If code will be useful, Please cite this paper
https://link.springer.com/chapter/10.1007/978-981-16-7167-8_77
About
No description, website, or topics provided.
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6 stars
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3 forks
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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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ABIDE_Classification_DLModels
Please download the ABIDE dataset from the following link.
https://www.nitrc.org/ir/app/template/XDATScreen_report_xnat_projectData.vm/search_element/xnat:projectData/search_field/xnat:projectData.ID/search_value/ABIDE
Use data preprocess file to convert all data using different atlases and save into numpy for each atlas
You can get the different atlases from the following link
https://nilearn.github.io/modules/generated/nilearn.plotting.plot_prob_atlas.html
https://github.com/Parietal-INRIA/DiFuMo
Use data generator to convert data into batches
train, test and optimize the deep learning models.
We will update more results and evaluation code after accepting of the paper.
All codes are written in python and all deep learning models are written in PyTorch library.
https://pytorch.org/
https://numpy.org/doc/stable/reference/index.html
If code will be useful, Please cite this paper
https://link.springer.com/chapter/10.1007/978-981-16-7167-8_77
About
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6 stars
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1 watching
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3 forks
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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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ABIDE_Classification_DLModels
Please download the ABIDE dataset from the following link.
https://www.nitrc.org/ir/app/template/XDATScreen_report_xnat_projectData.vm/search_element/xnat:projectData/search_field/xnat:projectData.ID/search_value/ABIDE
Use data preprocess file to convert all data using different atlases and save into numpy for each atlas
You can get the different atlases from the following link
https://nilearn.github.io/modules/generated/nilearn.plotting.plot_prob_atlas.html
https://github.com/Parietal-INRIA/DiFuMo
Use data generator to convert data into batches
train, test and optimize the deep learning models.
We will update more results and evaluation code after accepting of the paper.
All codes are written in python and all deep learning models are written in PyTorch library.
https://pytorch.org/
https://numpy.org/doc/stable/reference/index.html
If code will be useful, Please cite this paper
https://link.springer.com/chapter/10.1007/978-981-16-7167-8_77
About
No description, website, or topics provided.
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6 stars
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1 watching
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3 forks
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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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ABIDE_Classification_DLModels
Please download the ABIDE dataset from the following link.
https://www.nitrc.org/ir/app/template/XDATScreen_report_xnat_projectData.vm/search_element/xnat:projectData/search_field/xnat:projectData.ID/search_value/ABIDE
Use data preprocess file to convert all data using different atlases and save into numpy for each atlas
You can get the different atlases from the following link
https://nilearn.github.io/modules/generated/nilearn.plotting.plot_prob_atlas.html
https://github.com/Parietal-INRIA/DiFuMo
Use data generator to convert data into batches
train, test and optimize the deep learning models.
We will update more results and evaluation code after accepting of the paper.
All codes are written in python and all deep learning models are written in PyTorch library.
https://pytorch.org/
https://numpy.org/doc/stable/reference/index.html
If code will be useful, Please cite this paper
https://link.springer.com/chapter/10.1007/978-981-16-7167-8_77
About
No description, website, or topics provided.
Resources
Readme
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6 stars
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1 watching
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3 forks
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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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ABIDE_Classification_DLModels
Please download the ABIDE dataset from the following link.
https://www.nitrc.org/ir/app/template/XDATScreen_report_xnat_projectData.vm/search_element/xnat:projectData/search_field/xnat:projectData.ID/search_value/ABIDE
Use data preprocess file to convert all data using different atlases and save into numpy for each atlas
You can get the different atlases from the following link
https://nilearn.github.io/modules/generated/nilearn.plotting.plot_prob_atlas.html
https://github.com/Parietal-INRIA/DiFuMo
Use data generator to convert data into batches
train, test and optimize the deep learning models.
We will update more results and evaluation code after accepting of the paper.
All codes are written in python and all deep learning models are written in PyTorch library.
https://pytorch.org/
https://numpy.org/doc/stable/reference/index.html
If code will be useful, Please cite this paper
https://link.springer.com/chapter/10.1007/978-981-16-7167-8_77
About
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3 forks
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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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ABIDE_Classification_DLModels
Please download the ABIDE dataset from the following link.
https://www.nitrc.org/ir/app/template/XDATScreen_report_xnat_projectData.vm/search_element/xnat:projectData/search_field/xnat:projectData.ID/search_value/ABIDE
Use data preprocess file to convert all data using different atlases and save into numpy for each atlas
You can get the different atlases from the following link
https://nilearn.github.io/modules/generated/nilearn.plotting.plot_prob_atlas.html
https://github.com/Parietal-INRIA/DiFuMo
Use data generator to convert data into batches
train, test and optimize the deep learning models.
We will update more results and evaluation code after accepting of the paper.
All codes are written in python and all deep learning models are written in PyTorch library.
https://pytorch.org/
https://numpy.org/doc/stable/reference/index.html
If code will be useful, Please cite this paper
https://link.springer.com/chapter/10.1007/978-981-16-7167-8_77
About
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6 stars
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