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cs.CV2026

LETT-NeXt: A Lightweight RECIST-Guided Model for 3D CT Lesion Segmentation

Sebastian Aas, Elias Stenhede, Arian Ranjbar

RECIST diameter measurements are widely used for tumor response assessment, but they provide only a limited 2D description of lesion extent. We present LETT-NeXt, a lightweight REC…

cs.CV2026

EchoXFlow: A Beamspace Echocardiography Dataset for Cardiac Motion, Flow, and Function

Elias Stenhede, Joanna Sulkowska, Eivind Bjørkan Orstad +2

We introduce EchoXFlow, a clinical echocardiography dataset for learning from ultrasound in its native acquisition geometry rather than from scan-converted Cartesian videos. Existi…

cs.CV2026

Biomarker-Based Pretraining for Chagas Disease Screening in Electrocardiograms

Elias Stenhede, Arian Ranjbar

Chagas disease screening via ECGs is limited by scarce and noisy labels in existing datasets. We propose a biomarker-based pretraining approach, where an ECG feature extractor is f…

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…