Published in Machine Learning: Health https://iopscience.iop.org/article/10.1088/3049-477X/ae4984 By Renee Zacharowicz, Yevgeniy A Men, Luis Filipe Nakayama and Joachim A Behar Research overview: Open retinal fundus image datasets have been a gift to the diabetic retinopathy (DR) community—but they come with a catch: the labels are often inconsistent, noisy, or simply wrong. And when your goal is fine-grained DR staging  (not just “disease vs. no disease”), label noise can qu

By Márton Áron Goda, Arie Oksenberg, Ali Azarbarzin and Joachim A Behar Publication: https://iopscience.iop.org/article/10.1088/1361-6579/ae3ef0/meta Pyppg toolbox used in the analysis: https://pyppg.readthedocs.io/en/latest/ Can a smartwatch tell the difference between apnea and hypopnea? Wearables already track heart rate, sleep timing, and sometimes even blood oxygen. But obstructive sleep apnea (OSA) isn’t just “events happened” vs. “events didn’t happen”—there are differ

By Benjamin A Cohen, Jonathan Fhima, Meishar Meisel, Baskin Meital, Luis Filipe Nakayama, Eran Berkowitz and Joachim A Behar URL: https://iopscience.iop.org/article/10.1088/1361-6579/ae3936 Part of the Lirot.ai project: https://www.aimlab-technion.com/lirot-ai Do we really need “retina-only” foundation models to detect AMD? Age-related macular degeneration (AMD) is a leading cause of irreversible vision loss, and scaling reliable screening from routine retinal photos (digit

This work represents a close collaboration between the following authors: Shany Brimer Biton, Jonathan Sobel, Anat Reiner Benaim, Eran Zvuloni, Ronit Almog, Julien Oster, Izhar Laufer, Ilan Green, Mahmoud Suleiman, Kenta Tsutsui, Leif Sörnmo, and Joachim A. Behar. Publication: https://iopscience.iop.org/article/10.1088/3049-477X/ae375f Models available on GitHub: https://github.com/aim-lab/arnet2 The collaboration brings together expertise from the Faculty of Biomedical Engin

By Hagai Hamami, Yosef Solewicz, Daniel Zur, Yonatan Kleerekoper and Joachim A. Behar Published paper (IEEE): https://ieeexplore.ieee.org/document/11359459 Preprint: https://arxiv.org/abs/2506.11238 Premature Ventricular Contractions (PVCs) are among the most common abnormal heartbeats, yet they are notoriously difficult to detect automatically. Their appearance shifts across patients, lead configurations, sensor types, and recording environments. This variability has long pr