3 papers
cs.CV2025
Lite ENSAM: a lightweight cancer segmentation model for 3D Computed Tomography
Agnar Martin Bjørnstad, Elias Stenhede, Arian Ranjbar
Accurate tumor size measurement is a cornerstone of evaluating cancer treatment response. The most widely adopted standard for this purpose is the Response Evaluation Criteria in S…
cs.CV2025
Digitizing Paper ECGs at Scale: An Open-Source Algorithm for Clinical Research
Elias Stenhede, Agnar Martin Bjørnstad, Arian Ranjbar
Millions of clinical ECGs exist only as paper scans, making them unusable for modern automated diagnostics. We introduce a fully automated, modular framework that converts scanned…
cs.CV2025
ENSAM: an efficient foundation model for interactive segmentation of 3D medical images
Elias Stenhede, Agnar Martin Bjørnstad, Arian Ranjbar
We present ENSAM (Equivariant, Normalized, Segment Anything Model), a lightweight and promptable model for universal 3D medical image segmentation. ENSAM combines a SegResNet-based…