BranchesTags

Repository files navigation

UnCLe SAM -- Unleashing Continual Learning for SAM (MIDL 2024)

This repository represents the official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. For more details, please refer to our paper.

Table Of Contents

  1. Introduction
  2. Installation
  3. How to get started?
  4. Data and pre-trained models
  5. Citations
  6. License

Introduction

This MIDL 2024 submission currently includes the following methods for Continual Learning:

  • Sequential Training
  • Riemannian Walk
  • Elastic Weight Consolidation

Installation

The simplest way to install all dependencies is by using Anaconda:

  1. Create a Python 3.9 environment as conda create -n <your_conda_env> python=3.9 and activate it as conda activate <your_conda_env>.
  2. Install CUDA and PyTorch through conda with the command specified by PyTorch. The command for Linux was at the time conda install pytorch torchvision cudatoolkit=11.3 -c pytorch. Our code was last tested with version 1.13. Pytorch and TorchVision versions can be specified during the installation as conda install pytorch==<X.X.X> torchvision==<X.X.X> cudatoolkit=<X.X> -c pytorch. Note that the cudatoolkit version should be of the same major version as the CUDA version installed on the machine, e.g. when using CUDA 11.x one should install a cudatoolkit 11.x version, but not a cudatoolkit 10.x version.
  3. Navigate to the project root (where setup.py lives).
  4. Execute pip install -r requirements.txt to install all required packages.

How to get started?

  • The easiest way to start is using our train_abstract_*.py python files. For every baseline and Continual Learning method, we provide specific train_abstract_*.py python files, located in the scripts folder.
  • The eval folder contains several jupyter notebooks that were used to calculate performance metrics and plots used in our submission.

Data and pre-trained models

For more information about UnCLe SAM, please read the following paper:

Ranem, A., Aflal, M. A. M., Fuchs, M., & Mukhopadhyay, A. (2024, February).
UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. In Medical Imaging with Deep Learning.

Citations

If you are using UnCLe SAM or our code base for your article, please cite the following paper:

@inproceedings{ranem2024uncle,
title={UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation},
author={Ranem, Amin and Aflal, Mohamed Afham Mohamed and Fuchs, Moritz and Mukhopadhyay, Anirban},
booktitle={Medical Imaging with Deep Learning},
year={2024}
}

License

Apache License 2.0

About

Official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation.

Topics

continual-learningsegment-anything-model

Resources

Readme
Apache-2.0 license
Activity
Custom properties

Stars

3 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, '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
BranchesTags
Repository files navigation
UnCLe SAM -- Unleashing Continual Learning for SAM (MIDL 2024)
This repository represents the official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. For more details, please refer to our paper.
Table Of Contents
  1. Introduction
  2. Installation
  3. How to get started?
  4. Data and pre-trained models
  5. Citations
  6. License
Introduction
This MIDL 2024 submission currently includes the following methods for Continual Learning:
  • Sequential Training
  • Riemannian Walk
  • Elastic Weight Consolidation
Installation
The simplest way to install all dependencies is by using Anaconda:
  1. Create a Python 3.9 environment as conda create -n <your_conda_env> python=3.9 and activate it as conda activate <your_conda_env>.
  2. Install CUDA and PyTorch through conda with the command specified by PyTorch. The command for Linux was at the time conda install pytorch torchvision cudatoolkit=11.3 -c pytorch. Our code was last tested with version 1.13. Pytorch and TorchVision versions can be specified during the installation as conda install pytorch==<X.X.X> torchvision==<X.X.X> cudatoolkit=<X.X> -c pytorch. Note that the cudatoolkit version should be of the same major version as the CUDA version installed on the machine, e.g. when using CUDA 11.x one should install a cudatoolkit 11.x version, but not a cudatoolkit 10.x version.
  3. Navigate to the project root (where setup.py lives).
  4. Execute pip install -r requirements.txt to install all required packages.
How to get started?
  • The easiest way to start is using our train_abstract_*.py python files. For every baseline and Continual Learning method, we provide specific train_abstract_*.py python files, located in the scripts folder.
  • The eval folder contains several jupyter notebooks that were used to calculate performance metrics and plots used in our submission.
Data and pre-trained models
For more information about UnCLe SAM, please read the following paper:
Ranem, A., Aflal, M. A. M., Fuchs, M., & Mukhopadhyay, A. (2024, February).
UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. In Medical Imaging with Deep Learning.

Citations
If you are using UnCLe SAM or our code base for your article, please cite the following paper:
@inproceedings{ranem2024uncle,
title={UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation},
author={Ranem, Amin and Aflal, Mohamed Afham Mohamed and Fuchs, Moritz and Mukhopadhyay, Anirban},
booktitle={Medical Imaging with Deep Learning},
year={2024}
}

License
Apache License 2.0
About
Official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation.
Topics
continual-learningsegment-anything-model
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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
BranchesTags
Repository files navigation
UnCLe SAM -- Unleashing Continual Learning for SAM (MIDL 2024)
This repository represents the official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. For more details, please refer to our paper.
Table Of Contents
  1. Introduction
  2. Installation
  3. How to get started?
  4. Data and pre-trained models
  5. Citations
  6. License
Introduction
This MIDL 2024 submission currently includes the following methods for Continual Learning:
  • Sequential Training
  • Riemannian Walk
  • Elastic Weight Consolidation
Installation
The simplest way to install all dependencies is by using Anaconda:
  1. Create a Python 3.9 environment as conda create -n <your_conda_env> python=3.9 and activate it as conda activate <your_conda_env>.
  2. Install CUDA and PyTorch through conda with the command specified by PyTorch. The command for Linux was at the time conda install pytorch torchvision cudatoolkit=11.3 -c pytorch. Our code was last tested with version 1.13. Pytorch and TorchVision versions can be specified during the installation as conda install pytorch==<X.X.X> torchvision==<X.X.X> cudatoolkit=<X.X> -c pytorch. Note that the cudatoolkit version should be of the same major version as the CUDA version installed on the machine, e.g. when using CUDA 11.x one should install a cudatoolkit 11.x version, but not a cudatoolkit 10.x version.
  3. Navigate to the project root (where setup.py lives).
  4. Execute pip install -r requirements.txt to install all required packages.
How to get started?
  • The easiest way to start is using our train_abstract_*.py python files. For every baseline and Continual Learning method, we provide specific train_abstract_*.py python files, located in the scripts folder.
  • The eval folder contains several jupyter notebooks that were used to calculate performance metrics and plots used in our submission.
Data and pre-trained models
For more information about UnCLe SAM, please read the following paper:
Ranem, A., Aflal, M. A. M., Fuchs, M., & Mukhopadhyay, A. (2024, February).
UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. In Medical Imaging with Deep Learning.

Citations
If you are using UnCLe SAM or our code base for your article, please cite the following paper:
@inproceedings{ranem2024uncle,
title={UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation},
author={Ranem, Amin and Aflal, Mohamed Afham Mohamed and Fuchs, Moritz and Mukhopadhyay, Anirban},
booktitle={Medical Imaging with Deep Learning},
year={2024}
}

License
Apache License 2.0
About
Official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation.
Topics
continual-learningsegment-anything-model
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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
BranchesTags
Repository files navigation
UnCLe SAM -- Unleashing Continual Learning for SAM (MIDL 2024)
This repository represents the official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. For more details, please refer to our paper.
Table Of Contents
  1. Introduction
  2. Installation
  3. How to get started?
  4. Data and pre-trained models
  5. Citations
  6. License
Introduction
This MIDL 2024 submission currently includes the following methods for Continual Learning:
  • Sequential Training
  • Riemannian Walk
  • Elastic Weight Consolidation
Installation
The simplest way to install all dependencies is by using Anaconda:
  1. Create a Python 3.9 environment as conda create -n <your_conda_env> python=3.9 and activate it as conda activate <your_conda_env>.
  2. Install CUDA and PyTorch through conda with the command specified by PyTorch. The command for Linux was at the time conda install pytorch torchvision cudatoolkit=11.3 -c pytorch. Our code was last tested with version 1.13. Pytorch and TorchVision versions can be specified during the installation as conda install pytorch==<X.X.X> torchvision==<X.X.X> cudatoolkit=<X.X> -c pytorch. Note that the cudatoolkit version should be of the same major version as the CUDA version installed on the machine, e.g. when using CUDA 11.x one should install a cudatoolkit 11.x version, but not a cudatoolkit 10.x version.
  3. Navigate to the project root (where setup.py lives).
  4. Execute pip install -r requirements.txt to install all required packages.
How to get started?
  • The easiest way to start is using our train_abstract_*.py python files. For every baseline and Continual Learning method, we provide specific train_abstract_*.py python files, located in the scripts folder.
  • The eval folder contains several jupyter notebooks that were used to calculate performance metrics and plots used in our submission.
Data and pre-trained models
For more information about UnCLe SAM, please read the following paper:
Ranem, A., Aflal, M. A. M., Fuchs, M., & Mukhopadhyay, A. (2024, February).
UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. In Medical Imaging with Deep Learning.

Citations
If you are using UnCLe SAM or our code base for your article, please cite the following paper:
@inproceedings{ranem2024uncle,
title={UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation},
author={Ranem, Amin and Aflal, Mohamed Afham Mohamed and Fuchs, Moritz and Mukhopadhyay, Anirban},
booktitle={Medical Imaging with Deep Learning},
year={2024}
}

License
Apache License 2.0
About
Official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation.
Topics
continual-learningsegment-anything-model
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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
BranchesTags
Repository files navigation
UnCLe SAM -- Unleashing Continual Learning for SAM (MIDL 2024)
This repository represents the official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. For more details, please refer to our paper.
Table Of Contents
  1. Introduction
  2. Installation
  3. How to get started?
  4. Data and pre-trained models
  5. Citations
  6. License
Introduction
This MIDL 2024 submission currently includes the following methods for Continual Learning:
  • Sequential Training
  • Riemannian Walk
  • Elastic Weight Consolidation
Installation
The simplest way to install all dependencies is by using Anaconda:
  1. Create a Python 3.9 environment as conda create -n <your_conda_env> python=3.9 and activate it as conda activate <your_conda_env>.
  2. Install CUDA and PyTorch through conda with the command specified by PyTorch. The command for Linux was at the time conda install pytorch torchvision cudatoolkit=11.3 -c pytorch. Our code was last tested with version 1.13. Pytorch and TorchVision versions can be specified during the installation as conda install pytorch==<X.X.X> torchvision==<X.X.X> cudatoolkit=<X.X> -c pytorch. Note that the cudatoolkit version should be of the same major version as the CUDA version installed on the machine, e.g. when using CUDA 11.x one should install a cudatoolkit 11.x version, but not a cudatoolkit 10.x version.
  3. Navigate to the project root (where setup.py lives).
  4. Execute pip install -r requirements.txt to install all required packages.
How to get started?
  • The easiest way to start is using our train_abstract_*.py python files. For every baseline and Continual Learning method, we provide specific train_abstract_*.py python files, located in the scripts folder.
  • The eval folder contains several jupyter notebooks that were used to calculate performance metrics and plots used in our submission.
Data and pre-trained models
For more information about UnCLe SAM, please read the following paper:
Ranem, A., Aflal, M. A. M., Fuchs, M., & Mukhopadhyay, A. (2024, February).
UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. In Medical Imaging with Deep Learning.

Citations
If you are using UnCLe SAM or our code base for your article, please cite the following paper:
@inproceedings{ranem2024uncle,
title={UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation},
author={Ranem, Amin and Aflal, Mohamed Afham Mohamed and Fuchs, Moritz and Mukhopadhyay, Anirban},
booktitle={Medical Imaging with Deep Learning},
year={2024}
}

License
Apache License 2.0
About
Official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation.
Topics
continual-learningsegment-anything-model
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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
BranchesTags
Repository files navigation
UnCLe SAM -- Unleashing Continual Learning for SAM (MIDL 2024)
This repository represents the official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. For more details, please refer to our paper.
Table Of Contents
  1. Introduction
  2. Installation
  3. How to get started?
  4. Data and pre-trained models
  5. Citations
  6. License
Introduction
This MIDL 2024 submission currently includes the following methods for Continual Learning:
  • Sequential Training
  • Riemannian Walk
  • Elastic Weight Consolidation
Installation
The simplest way to install all dependencies is by using Anaconda:
  1. Create a Python 3.9 environment as conda create -n <your_conda_env> python=3.9 and activate it as conda activate <your_conda_env>.
  2. Install CUDA and PyTorch through conda with the command specified by PyTorch. The command for Linux was at the time conda install pytorch torchvision cudatoolkit=11.3 -c pytorch. Our code was last tested with version 1.13. Pytorch and TorchVision versions can be specified during the installation as conda install pytorch==<X.X.X> torchvision==<X.X.X> cudatoolkit=<X.X> -c pytorch. Note that the cudatoolkit version should be of the same major version as the CUDA version installed on the machine, e.g. when using CUDA 11.x one should install a cudatoolkit 11.x version, but not a cudatoolkit 10.x version.
  3. Navigate to the project root (where setup.py lives).
  4. Execute pip install -r requirements.txt to install all required packages.
How to get started?
  • The easiest way to start is using our train_abstract_*.py python files. For every baseline and Continual Learning method, we provide specific train_abstract_*.py python files, located in the scripts folder.
  • The eval folder contains several jupyter notebooks that were used to calculate performance metrics and plots used in our submission.
Data and pre-trained models
For more information about UnCLe SAM, please read the following paper:
Ranem, A., Aflal, M. A. M., Fuchs, M., & Mukhopadhyay, A. (2024, February).
UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. In Medical Imaging with Deep Learning.

Citations
If you are using UnCLe SAM or our code base for your article, please cite the following paper:
@inproceedings{ranem2024uncle,
title={UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation},
author={Ranem, Amin and Aflal, Mohamed Afham Mohamed and Fuchs, Moritz and Mukhopadhyay, Anirban},
booktitle={Medical Imaging with Deep Learning},
year={2024}
}

License
Apache License 2.0
About
Official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation.
Topics
continual-learningsegment-anything-model
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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
BranchesTags
Repository files navigation
UnCLe SAM -- Unleashing Continual Learning for SAM (MIDL 2024)
This repository represents the official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. For more details, please refer to our paper.
Table Of Contents
  1. Introduction
  2. Installation
  3. How to get started?
  4. Data and pre-trained models
  5. Citations
  6. License
Introduction
This MIDL 2024 submission currently includes the following methods for Continual Learning:
  • Sequential Training
  • Riemannian Walk
  • Elastic Weight Consolidation
Installation
The simplest way to install all dependencies is by using Anaconda:
  1. Create a Python 3.9 environment as conda create -n <your_conda_env> python=3.9 and activate it as conda activate <your_conda_env>.
  2. Install CUDA and PyTorch through conda with the command specified by PyTorch. The command for Linux was at the time conda install pytorch torchvision cudatoolkit=11.3 -c pytorch. Our code was last tested with version 1.13. Pytorch and TorchVision versions can be specified during the installation as conda install pytorch==<X.X.X> torchvision==<X.X.X> cudatoolkit=<X.X> -c pytorch. Note that the cudatoolkit version should be of the same major version as the CUDA version installed on the machine, e.g. when using CUDA 11.x one should install a cudatoolkit 11.x version, but not a cudatoolkit 10.x version.
  3. Navigate to the project root (where setup.py lives).
  4. Execute pip install -r requirements.txt to install all required packages.
How to get started?
  • The easiest way to start is using our train_abstract_*.py python files. For every baseline and Continual Learning method, we provide specific train_abstract_*.py python files, located in the scripts folder.
  • The eval folder contains several jupyter notebooks that were used to calculate performance metrics and plots used in our submission.
Data and pre-trained models
For more information about UnCLe SAM, please read the following paper:
Ranem, A., Aflal, M. A. M., Fuchs, M., & Mukhopadhyay, A. (2024, February).
UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. In Medical Imaging with Deep Learning.

Citations
If you are using UnCLe SAM or our code base for your article, please cite the following paper:
@inproceedings{ranem2024uncle,
title={UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation},
author={Ranem, Amin and Aflal, Mohamed Afham Mohamed and Fuchs, Moritz and Mukhopadhyay, Anirban},
booktitle={Medical Imaging with Deep Learning},
year={2024}
}

License
Apache License 2.0
About
Official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation.
Topics
continual-learningsegment-anything-model
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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
BranchesTags
Repository files navigation
UnCLe SAM -- Unleashing Continual Learning for SAM (MIDL 2024)
This repository represents the official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. For more details, please refer to our paper.
Table Of Contents
  1. Introduction
  2. Installation
  3. How to get started?
  4. Data and pre-trained models
  5. Citations
  6. License
Introduction
This MIDL 2024 submission currently includes the following methods for Continual Learning:
  • Sequential Training
  • Riemannian Walk
  • Elastic Weight Consolidation
Installation
The simplest way to install all dependencies is by using Anaconda:
  1. Create a Python 3.9 environment as conda create -n <your_conda_env> python=3.9 and activate it as conda activate <your_conda_env>.
  2. Install CUDA and PyTorch through conda with the command specified by PyTorch. The command for Linux was at the time conda install pytorch torchvision cudatoolkit=11.3 -c pytorch. Our code was last tested with version 1.13. Pytorch and TorchVision versions can be specified during the installation as conda install pytorch==<X.X.X> torchvision==<X.X.X> cudatoolkit=<X.X> -c pytorch. Note that the cudatoolkit version should be of the same major version as the CUDA version installed on the machine, e.g. when using CUDA 11.x one should install a cudatoolkit 11.x version, but not a cudatoolkit 10.x version.
  3. Navigate to the project root (where setup.py lives).
  4. Execute pip install -r requirements.txt to install all required packages.
How to get started?
  • The easiest way to start is using our train_abstract_*.py python files. For every baseline and Continual Learning method, we provide specific train_abstract_*.py python files, located in the scripts folder.
  • The eval folder contains several jupyter notebooks that were used to calculate performance metrics and plots used in our submission.
Data and pre-trained models
For more information about UnCLe SAM, please read the following paper:
Ranem, A., Aflal, M. A. M., Fuchs, M., & Mukhopadhyay, A. (2024, February).
UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation. In Medical Imaging with Deep Learning.

Citations
If you are using UnCLe SAM or our code base for your article, please cite the following paper:
@inproceedings{ranem2024uncle,
title={UnCLe SAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation},
author={Ranem, Amin and Aflal, Mohamed Afham Mohamed and Fuchs, Moritz and Mukhopadhyay, Anirban},
booktitle={Medical Imaging with Deep Learning},
year={2024}
}

License
Apache License 2.0
About
Official PyTorch code base for our MIDL 2024 published paper UnCLeSAM: Unleashing SAM’s Potential for Continual Prostate MRI Segmentation.
Topics
continual-learningsegment-anything-model
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages