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Segment Anything Model (SAM) — Research Project

Research project focused on understanding SAM comprehensively and improving its performance for medical image segmentation.

Contents

FileDescription
2304.02643v1.pdfOriginal SAM paper (Kirillov et al., Meta AI, April 2023)
SAM_Comprehensive_Analysis.mdArchitecture, training, limitations, and medical improvement strategies
SAM_Mask_Decoder_Deep_Dive.mdDetailed walkthrough of the mask decoder
MedSAM_Analysis.mdMedSAM findings, limitations, and research opportunities
SAM2_Colab_Notebook.ipynbInteractive Colab notebook — load SAM2 from 🤗 Hugging Face

Quick Start

  1. Upload SAM2_Colab_Notebook.ipynb to Google Colab
  2. Runtime → Change runtime type → GPU (T4)
  3. Run all cells

The notebook loads SAM2.1 models directly from Hugging Face (facebook/sam2.1-hiera-{tiny,small,base-plus,large}) — no manual downloads needed.

Research Focus

  • Problem: SAM was trained on SA-1B (natural images only) → poor performance on medical images
  • Approach: LoRA fine-tuning, adapters, multi-scale features, automated prompting, test-time augmentation
  • Target modalities: CT, MRI, X-ray, pathology, ultrasound

Key References

About

Deep dive into Segment Anything Model and experiment improvement on medical image or other domain specific images like agriculture, architecture, micro biology etc. Explored techniques like FineTuning, RAG to improve performance to build domain expert on top of foundational model

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Readme
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GitHub - DreamRunnerMoshi/AppliedSAM: Deep dive into Segment Anything Model and experiment improvement on medical image or other domain specific images like agriculture, architecture, micro biology etc. Explored techniques like FineTuning, RAG to improve performance to build domain expert on top of foundational model · GitHub
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Segment Anything Model (SAM) — Research Project
Research project focused on understanding SAM comprehensively and improving its performance for medical image segmentation.
Contents
FileDescription
2304.02643v1.pdfOriginal SAM paper (Kirillov et al., Meta AI, April 2023)
SAM_Comprehensive_Analysis.mdArchitecture, training, limitations, and medical improvement strategies
SAM_Mask_Decoder_Deep_Dive.mdDetailed walkthrough of the mask decoder
MedSAM_Analysis.mdMedSAM findings, limitations, and research opportunities
SAM2_Colab_Notebook.ipynbInteractive Colab notebook — load SAM2 from 🤗 Hugging Face
Quick Start
  1. Upload SAM2_Colab_Notebook.ipynb to Google Colab
  2. Runtime → Change runtime type → GPU (T4)
  3. Run all cells
The notebook loads SAM2.1 models directly from Hugging Face (facebook/sam2.1-hiera-{tiny,small,base-plus,large}) — no manual downloads needed.
Research Focus
  • Problem: SAM was trained on SA-1B (natural images only) → poor performance on medical images
  • Approach: LoRA fine-tuning, adapters, multi-scale features, automated prompting, test-time augmentation
  • Target modalities: CT, MRI, X-ray, pathology, ultrasound
Key References
About
Deep dive into Segment Anything Model and experiment improvement on medical image or other domain specific images like agriculture, architecture, micro biology etc. Explored techniques like FineTuning, RAG to improve performance to build domain expert on top of foundational model
Resources
Readme
Activity
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GitHub - DreamRunnerMoshi/AppliedSAM: Deep dive into Segment Anything Model and experiment improvement on medical image or other domain specific images like agriculture, architecture, micro biology etc. Explored techniques like FineTuning, RAG to improve performance to build domain expert on top of foundational model · GitHub
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Segment Anything Model (SAM) — Research Project
Research project focused on understanding SAM comprehensively and improving its performance for medical image segmentation.
Contents
FileDescription
2304.02643v1.pdfOriginal SAM paper (Kirillov et al., Meta AI, April 2023)
SAM_Comprehensive_Analysis.mdArchitecture, training, limitations, and medical improvement strategies
SAM_Mask_Decoder_Deep_Dive.mdDetailed walkthrough of the mask decoder
MedSAM_Analysis.mdMedSAM findings, limitations, and research opportunities
SAM2_Colab_Notebook.ipynbInteractive Colab notebook — load SAM2 from 🤗 Hugging Face
Quick Start
  1. Upload SAM2_Colab_Notebook.ipynb to Google Colab
  2. Runtime → Change runtime type → GPU (T4)
  3. Run all cells
The notebook loads SAM2.1 models directly from Hugging Face (facebook/sam2.1-hiera-{tiny,small,base-plus,large}) — no manual downloads needed.
Research Focus
  • Problem: SAM was trained on SA-1B (natural images only) → poor performance on medical images
  • Approach: LoRA fine-tuning, adapters, multi-scale features, automated prompting, test-time augmentation
  • Target modalities: CT, MRI, X-ray, pathology, ultrasound
Key References
About
Deep dive into Segment Anything Model and experiment improvement on medical image or other domain specific images like agriculture, architecture, micro biology etc. Explored techniques like FineTuning, RAG to improve performance to build domain expert on top of foundational model
Resources
Readme
Activity
Stars
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GitHub - DreamRunnerMoshi/AppliedSAM: Deep dive into Segment Anything Model and experiment improvement on medical image or other domain specific images like agriculture, architecture, micro biology etc. Explored techniques like FineTuning, RAG to improve performance to build domain expert on top of foundational model · GitHub
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Segment Anything Model (SAM) — Research Project
Research project focused on understanding SAM comprehensively and improving its performance for medical image segmentation.
Contents
FileDescription
2304.02643v1.pdfOriginal SAM paper (Kirillov et al., Meta AI, April 2023)
SAM_Comprehensive_Analysis.mdArchitecture, training, limitations, and medical improvement strategies
SAM_Mask_Decoder_Deep_Dive.mdDetailed walkthrough of the mask decoder
MedSAM_Analysis.mdMedSAM findings, limitations, and research opportunities
SAM2_Colab_Notebook.ipynbInteractive Colab notebook — load SAM2 from 🤗 Hugging Face
Quick Start
  1. Upload SAM2_Colab_Notebook.ipynb to Google Colab
  2. Runtime → Change runtime type → GPU (T4)
  3. Run all cells
The notebook loads SAM2.1 models directly from Hugging Face (facebook/sam2.1-hiera-{tiny,small,base-plus,large}) — no manual downloads needed.
Research Focus
  • Problem: SAM was trained on SA-1B (natural images only) → poor performance on medical images
  • Approach: LoRA fine-tuning, adapters, multi-scale features, automated prompting, test-time augmentation
  • Target modalities: CT, MRI, X-ray, pathology, ultrasound
Key References
About
Deep dive into Segment Anything Model and experiment improvement on medical image or other domain specific images like agriculture, architecture, micro biology etc. Explored techniques like FineTuning, RAG to improve performance to build domain expert on top of foundational model
Resources
Readme
Activity
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GitHub - DreamRunnerMoshi/AppliedSAM: Deep dive into Segment Anything Model and experiment improvement on medical image or other domain specific images like agriculture, architecture, micro biology etc. Explored techniques like FineTuning, RAG to improve performance to build domain expert on top of foundational model · GitHub
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Segment Anything Model (SAM) — Research Project
Research project focused on understanding SAM comprehensively and improving its performance for medical image segmentation.
Contents
FileDescription
2304.02643v1.pdfOriginal SAM paper (Kirillov et al., Meta AI, April 2023)
SAM_Comprehensive_Analysis.mdArchitecture, training, limitations, and medical improvement strategies
SAM_Mask_Decoder_Deep_Dive.mdDetailed walkthrough of the mask decoder
MedSAM_Analysis.mdMedSAM findings, limitations, and research opportunities
SAM2_Colab_Notebook.ipynbInteractive Colab notebook — load SAM2 from 🤗 Hugging Face
Quick Start
  1. Upload SAM2_Colab_Notebook.ipynb to Google Colab
  2. Runtime → Change runtime type → GPU (T4)
  3. Run all cells
The notebook loads SAM2.1 models directly from Hugging Face (facebook/sam2.1-hiera-{tiny,small,base-plus,large}) — no manual downloads needed.
Research Focus
  • Problem: SAM was trained on SA-1B (natural images only) → poor performance on medical images
  • Approach: LoRA fine-tuning, adapters, multi-scale features, automated prompting, test-time augmentation
  • Target modalities: CT, MRI, X-ray, pathology, ultrasound
Key References
About
Deep dive into Segment Anything Model and experiment improvement on medical image or other domain specific images like agriculture, architecture, micro biology etc. Explored techniques like FineTuning, RAG to improve performance to build domain expert on top of foundational model
Resources
Readme
Activity
Stars
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GitHub - DreamRunnerMoshi/AppliedSAM: Deep dive into Segment Anything Model and experiment improvement on medical image or other domain specific images like agriculture, architecture, micro biology etc. Explored techniques like FineTuning, RAG to improve performance to build domain expert on top of foundational model · GitHub
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Segment Anything Model (SAM) — Research Project
Research project focused on understanding SAM comprehensively and improving its performance for medical image segmentation.
Contents
FileDescription
2304.02643v1.pdfOriginal SAM paper (Kirillov et al., Meta AI, April 2023)
SAM_Comprehensive_Analysis.mdArchitecture, training, limitations, and medical improvement strategies
SAM_Mask_Decoder_Deep_Dive.mdDetailed walkthrough of the mask decoder
MedSAM_Analysis.mdMedSAM findings, limitations, and research opportunities
SAM2_Colab_Notebook.ipynbInteractive Colab notebook — load SAM2 from 🤗 Hugging Face
Quick Start
  1. Upload SAM2_Colab_Notebook.ipynb to Google Colab
  2. Runtime → Change runtime type → GPU (T4)
  3. Run all cells
The notebook loads SAM2.1 models directly from Hugging Face (facebook/sam2.1-hiera-{tiny,small,base-plus,large}) — no manual downloads needed.
Research Focus
  • Problem: SAM was trained on SA-1B (natural images only) → poor performance on medical images
  • Approach: LoRA fine-tuning, adapters, multi-scale features, automated prompting, test-time augmentation
  • Target modalities: CT, MRI, X-ray, pathology, ultrasound
Key References
About
Deep dive into Segment Anything Model and experiment improvement on medical image or other domain specific images like agriculture, architecture, micro biology etc. Explored techniques like FineTuning, RAG to improve performance to build domain expert on top of foundational model
Resources
Readme
Activity
Stars
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GitHub - DreamRunnerMoshi/AppliedSAM: Deep dive into Segment Anything Model and experiment improvement on medical image or other domain specific images like agriculture, architecture, micro biology etc. Explored techniques like FineTuning, RAG to improve performance to build domain expert on top of foundational model · GitHub
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Segment Anything Model (SAM) — Research Project
Research project focused on understanding SAM comprehensively and improving its performance for medical image segmentation.
Contents
FileDescription
2304.02643v1.pdfOriginal SAM paper (Kirillov et al., Meta AI, April 2023)
SAM_Comprehensive_Analysis.mdArchitecture, training, limitations, and medical improvement strategies
SAM_Mask_Decoder_Deep_Dive.mdDetailed walkthrough of the mask decoder
MedSAM_Analysis.mdMedSAM findings, limitations, and research opportunities
SAM2_Colab_Notebook.ipynbInteractive Colab notebook — load SAM2 from 🤗 Hugging Face
Quick Start
  1. Upload SAM2_Colab_Notebook.ipynb to Google Colab
  2. Runtime → Change runtime type → GPU (T4)
  3. Run all cells
The notebook loads SAM2.1 models directly from Hugging Face (facebook/sam2.1-hiera-{tiny,small,base-plus,large}) — no manual downloads needed.
Research Focus
  • Problem: SAM was trained on SA-1B (natural images only) → poor performance on medical images
  • Approach: LoRA fine-tuning, adapters, multi-scale features, automated prompting, test-time augmentation
  • Target modalities: CT, MRI, X-ray, pathology, ultrasound
Key References
About
Deep dive into Segment Anything Model and experiment improvement on medical image or other domain specific images like agriculture, architecture, micro biology etc. Explored techniques like FineTuning, RAG to improve performance to build domain expert on top of foundational model
Resources
Readme
Activity
Stars
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0 forks
Report repository
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GitHub - DreamRunnerMoshi/AppliedSAM: Deep dive into Segment Anything Model and experiment improvement on medical image or other domain specific images like agriculture, architecture, micro biology etc. Explored techniques like FineTuning, RAG to improve performance to build domain expert on top of foundational model · GitHub
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Segment Anything Model (SAM) — Research Project
Research project focused on understanding SAM comprehensively and improving its performance for medical image segmentation.
Contents
FileDescription
2304.02643v1.pdfOriginal SAM paper (Kirillov et al., Meta AI, April 2023)
SAM_Comprehensive_Analysis.mdArchitecture, training, limitations, and medical improvement strategies
SAM_Mask_Decoder_Deep_Dive.mdDetailed walkthrough of the mask decoder
MedSAM_Analysis.mdMedSAM findings, limitations, and research opportunities
SAM2_Colab_Notebook.ipynbInteractive Colab notebook — load SAM2 from 🤗 Hugging Face
Quick Start
  1. Upload SAM2_Colab_Notebook.ipynb to Google Colab
  2. Runtime → Change runtime type → GPU (T4)
  3. Run all cells
The notebook loads SAM2.1 models directly from Hugging Face (facebook/sam2.1-hiera-{tiny,small,base-plus,large}) — no manual downloads needed.
Research Focus
  • Problem: SAM was trained on SA-1B (natural images only) → poor performance on medical images
  • Approach: LoRA fine-tuning, adapters, multi-scale features, automated prompting, test-time augmentation
  • Target modalities: CT, MRI, X-ray, pathology, ultrasound
Key References
About
Deep dive into Segment Anything Model and experiment improvement on medical image or other domain specific images like agriculture, architecture, micro biology etc. Explored techniques like FineTuning, RAG to improve performance to build domain expert on top of foundational model
Resources
Readme
Activity
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