Publication URL: [...] Authors: Renee Najman, Ben Gofrit, Benjamin Cohen, Or Abramovich, Meishar Meisel, Meital Baskin, Joel Hanhart, Fernando Malerbi, Assaf Kratz, Michael Waisbourd, Ari Leshno, Hadas Pizem, Dinah Zur, Nitsan Duvdevan-Strier, Luis Nakayama, Eran Berkowitz, and Joachim A. Behar We are pleased to share that our lab’s paper, “Benchmarking Lirot.ai Against Expert Ophthalmologists for Clinical Detection of Referable Eye Diseases,” has been published in IOP Machin

Eran Zvuloni, Ronit Almog, Michael Glikson, Shany Brimer Biton, Ilan Green, Izhar Laufer, Offer Amir, and Joachim A. Behar The collaborating institutions are Technion-Israel Institute of Technology, Rambam Health Care Campus, Shaare Zedek Medical Center, Leumit Health Services, Hadassah Medical Center, and the Hebrew University Faculty of Medicine. URL: XXX New publication from the Technion AIMLab introduces DeepHHF, an explainable deep learning model that uses 24-hour single

Raphael Judkiewicz, Adi Shaked, Nomi Friedmann, Doron Hanuka, Joel Hanhart and Joachim A Behar URL: https://iopscience.iop.org/article/10.1088/3049-477X/ae5aa5 Can the eye help detect stroke risk? A new AIMLab study explores carotid stenosis detection from routine retinal imaging At AIMLab at the Technion, we are interested in how artificial intelligence can turn routine medical data into meaningful clinical insight. In our latest study, we explored a simple but powerful que

By Raphael Judkiewicz, Eran Berkowitz, Meishar Meisel, Tomer Michaeli & Joachim A. Behar We’re excited to share our new publication, “Shifting the retinal foundation models paradigm from slices to volumes for optical coherence tomography,” now available in npj Digital Medicine. URL: https://www.nature.com/articles/s41746-026-02496-7 Optical coherence tomography, or OCT, is one of the most important imaging tools in ophthalmology. It gives clinicians a detailed cross-sectiona

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