Hugging Face Trending Papers

EchoRisk: A Multicentre Echocardiography Dataset and Benchmark for Cardio-Oncology

Read the original on Hugging Face Trending Papers →

Therapy-induced cardiotoxicity is the leading non-oncological cause of treatment interruption in breast cancer patients, yet early, automated risk stratification from routine cardiac imaging remains an unsolved problem. We present EchoRisk, the first curated, multicentre, longitudinal echocardiography dataset with explicit cardiotoxicity labels, released as the primary technical reference for the EchoRisk-MICCAI 2026 challenge.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv AI
Jul 2

EchoRisk: A Multicentre Echocardiography Dataset and Benchmark for Cardio-Oncology

arXiv:2607. 01039v1 Announce Type: cross Abstract: Therapy-induced cardiotoxicity is the leading non-oncological cause of treatment interruption in breast cancer patients, yet early, automated risk stratification from routine cardiac imaging remains an unsolved problem.

By Grigorios Kalliatakis, Georgia Karanasiou, Georgios Manikis, Manolis Tsiknakis, Dimitrios Fotiadis, Dorothea Tsekoura, Kalliopi Keramida, Vasileios Bouratzis, Lampros Lakkas, Katerina Naka, Andri Papakonstantinou, Anastasia Constantinidou, Kostas Marias
arXiv AI
Jun 2

CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations

arXiv:2606. 00123v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown strong performance on public medical benchmarks, yet existing evaluations often remain weak proxies for clinical use, relying on isolated inputs and simplified recognition-style tasks.

By Zixian Su, Hongkai Zhang, Fan Gao, Encheng Su, Taiping Qu, Jingwei Guo, Nan Zhang, Hui Wang, Zhen Zhou, Kairui Bo, Yan Chen, Yue Ren, Shuai Li, Lei Xu, Henggui Zhang
Hugging Face Trending Papers
6d ago

Intervention-Aware Clinical World Model for Post-Op Outcome Forecasting in Cardiology

Many clinical prediction models treat post-intervention outcomes as a one-step mapping from baseline measurements to a future endpoint. However, recovery after a procedure often unfolds as an irregular trajectory: clinical observations, medication changes, repeat interventions, and physiological measurements are recorded asynchronously and can change risk assessment over time.