medsam

Web-based semi-automated retinal fluid segmentation tool for OCT images. Combines traditional image processing with deep learning models (U-Net, MedSAM) to help medical professionals efficiently annotate and segment retinal fluid regions. Features interactive annotation tools, multiple segmentation algorithms, and user-friendly web interface.

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• CSS

Fine-tuning MedSAM and SAM for medical image segmentation with custom prompt strategies

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• Python

Parameter-efficient brain lesion segmentation using MedSAM and LoRA on the MICCAI WMH Challenge dataset.

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• Jupyter Notebook

Anatomy-guided Cross-modal Fusion for automated radiology report generation from DICOM CT scans. Built with CT-CLIP, MedSAM, GatorTron, and LLaMA-3 (LoRA).

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• Python

Benchmarking SAM and MedSAM on ISIC 2018 melanoma segmentation — with a focus on the deployment gap: published SAM papers measure performance using ground-truth-derived prompts that are unavailable in real clinical settings. This project quantifies that gap.

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• Python

This is the official repository of Team Anastasia, which achieved 1st place in ImageCLEF 2025 Dermatological Segmentation Challenge (Subtask 1)..

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• Python

Machine Learning for Medical Image Processing. Project done for Charité, the university hospital of Berlin. The aim was to segment coronary arteries and then extract a graph from it, in order to aid detection of coronary artery disease (CAD).

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• Python

Monorepo containing the frontend (Next.js), backend (Node.js + Express.js), and GPU inference service (TensorRT-optimized Python engine) for a cardiac segmentation application, streamlining local deployment.

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• PowerShell

A comprehensive benchmark study evaluating the robustness of Segment Anything Model (SAM) and its medical domain adaptation (MedSAM) under realistic noisy medical imaging conditions.

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• Jupyter Notebook