Anatomy-guided Cross-modal Fusion for automated radiology report generation from DICOM CT scans. Built with CT-CLIP, MedSAM, GatorTron, and LLaMA-3 (LoRA).
MedSAM enhanced with CLIP text descriptors for multi-organ medical image segmentation, evaluated on 24k labeled CT slices from FLARE 2022.
Fine-tuning MedSAM and SAM for medical image segmentation with custom prompt strategies
Reproducible, model-agnostic prompt evaluation for medical segmentation.
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).
This is the official repository of Team Anastasia, which achieved 1st place in ImageCLEF 2025 Dermatological Segmentation Challenge (Subtask 1)..
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.
Reproducible cardiac MRI SAX segmentation workflow with ACDC validation, Dice/HD95 metrics, QC overlays, and failure analysis
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