arXiv AI July 7, 2026 By Iman Islam, Esther Puyol-Ant\'on, Bram Ruijsink, Andrew J. Reader, Andrew P. King

Comparison of Loss Functions for Robust Deep Learning-based Echocardiography Segmentation when Learning with Partially Labelled Data from Multiple Domains

arXiv:2607. 05008v1 Announce Type: cross Abstract: Echocardiography is the first imaging modality used for assessing cardiac function, and accurate segmentation of cardiac structures is essential for deriving biomarkers.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

computer-vision

arXiv AI

Jul 7

SNR-Adaptive Unified Diffusion for Multi-Task Medical Image Segmentation

arXiv:2607. 03103v1 Announce Type: cross Abstract: Clinical cardiac imaging pipelines currently deploy separate models for each dataset and modality, incurring redundant training costs and precluding knowledge sharing across anatomically related tasks.

By Jiahao Liu, Hang Wei, Shuai Wu

diffusion computer-vision fine-tuning benchmarks safety

arXiv AI

Jun 29

EAGT: Echocardiography Augmentation for Generalisability and Transferability

arXiv:2605. 16427v2 Announce Type: replace-cross Abstract: Deep learning models for echocardiography segmentation often struggle to generalise across institutions, scanners, and patient populations, where collecting large, consistently annotated datasets is infeasible.

By Soroush Elyasi, Sara Adibzadeh, Nasim Dadashi Serej, Massoud Zolgharni

computer-vision

arXiv AI

Jun 9

Comparative evaluation of training strategies using partially labelled datasets for segmentation of white matter hyperintensities and stroke lesions in FLAIR MRI

arXiv:2601. 20503v2 Announce Type: replace-cross Abstract: White matter hyperintensities (WMH) and ischaemic stroke lesions (ISL) are key imaging biomarkers of cerebral small vessel disease (SVD) detectable on magnetic resonance imaging (MRI).

By Jesse Phitidis, Alison Q. Smithard, William N. Whiteley, Joanna M. Wardlaw, Miguel O. Bernabeu, Maria Vald\'es Hern\'andez

computer-vision efficiency

arXiv Machine Learning

Jun 2

Entropy Minimization without Model Collapse: Mitigating Prediction Bias in Medical Imaging

arXiv:2606. 02339v1 Announce Type: new Abstract: Entropy minimization (EM) is the dominant objective for test-time adaptation, yet its failure mode, model collapse, remains poorly understood.

By Tim Nielen, Sameer Ambekar, Johannes Kiechle, Daniel M. Lang, Julia A. Schnabel

benchmarks safety

arXiv Machine Learning

Aug 12

VIDS-Seg: Towards Reliable Uncertainty Quantification in Pediatric Cardiac Ultrasound Segmentation

arXiv:2608. 10903v1 Announce Type: cross Abstract: Reliable clinical deployment of machine learning requires models that know when they are likely to fail, particularly for subgroups underrepresented in training data.

By Paul Fischer, Ece Ozkan

computer-vision safety

arXiv AI

Jul 1

Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelines

arXiv:2605. 23995v4 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) is increasingly used in medical image analysis to reduce dependence on costly expert annotations by learning transferable representations from unlabeled data.

By Chathura Wimalasiri, Kishor Nandakishor, Marimuthu Palaniswami

computer-vision

Related stories

SNR-Adaptive Unified Diffusion for Multi-Task Medical Image Segmentation

EAGT: Echocardiography Augmentation for Generalisability and Transferability

Comparative evaluation of training strategies using partially labelled datasets for segmentation of white matter hyperintensities and stroke lesions in FLAIR MRI

Entropy Minimization without Model Collapse: Mitigating Prediction Bias in Medical Imaging

VIDS-Seg: Towards Reliable Uncertainty Quantification in Pediatric Cardiac Ultrasound Segmentation

Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelines