Deep Learning for Automatic Detection of Abnormal Findings in Breast Mammography. - Ayelet Akselrod-Ballin; Leonid Karlinsky; Alon Hazan; Ran Bakalo; Ami Ben Horesh; Yoel Shoshan; Ella Barkan (DLMIA/ML-CDS@MICCAI, 2017)

AdapterNet - learning input transformation for domain adaptation. - Alon Hazan; Yoel Shoshan; Daniel Khapun; Roy Aladjem; Vadim Ratner (CoRR, 2018)

Learning multiple non-mutually-exclusive tasks for improved classification of inherently ordered labels. - Vadim Ratner; Yoel Shoshan; Tal Kachman (CoRR, 2018)

Regularized adversarial examples for model interpretability. - Yoel Shoshan; Vadim Ratner (CoRR, 2018)

Mammogram Classification with Ordered Loss. - Rami Ben-Ari; Yoel Shoshan; Tal Tlusty (AIME, 2019)

The Case of Missed Cancers: Applying AI as a Radiologist's Safety Net. - Michal Chorev; Yoel Shoshan; Ayelet Akselrod-Ballin; Adam Spiro; Shaked Naor; Alon Hazan; Vesna Resende Barros; Iuliana Weinstein; Esma Herzel; Varda Shalev; Michal Guindy; Michal Rosen-Zvi (MICCAI (6), 2020)

An Ensemble of 3D U-Net Based Models for Segmentation of Kidney and Masses in CT Scans. - Alex Golts; Daniel Khapun; Daniel Shats; Yoel Shoshan; Flora Gilboa-Solomon (KiTS@MICCAI, 2021)

Beyond Non-maximum Suppression - Detecting Lesions in Digital Breast Tomosynthesis Volumes. - Yoel Shoshan; Aviad Zlotnick; Vadim Ratner; Daniel Khapun; Ella Barkan; Flora Gilboa-Solomon (MICCAI (5), 2021)

Lessons from the first DBTex Challenge. - Jungkyu Park 001; Yoel Shoshan; Robert Martí; Pablo Gómez del Campo; Vadim Ratner; Daniel Khapun; Aviad Zlotnick; Ella Barkan; Flora Gilboa-Solomon; Jakub Chledowski; Jan Witowski; Alexandra Millet; et al. (Nat. Mach. Intell., 2021)

On the Choice of Active Site Sequences for Kinase-Ligand Affinity Prediction. - Jannis Born; Yoel Shoshan; Tien Huynh; Wendy D. Cornell; Eric J. Martin; Matteo Manica (J. Chem. Inf. Model., 2022)

The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT. - Nicholas Heller; Fabian Isensee; Dasha Trofimova; Resha Tejpaul; Zhongchen Zhao; Huai Chen; Lisheng Wang; Alex Golts; Daniel Khapun; Daniel Shats; Yoel Shoshan; Flora Gilboa-Solomon; et al. (CoRR, 2023)

FuseMedML: a framework for accelerated discovery in machine learning based biomedicine. - Alex Golts; Moshe Raboh; Yoel Shoshan; Sagi Polaczek; Simona Rabinovici-Cohen; Efrat Hexter (J. Open Source Softw., 2023)

A large dataset curation and benchmark for drug target interaction. - Alex Golts; Vadim Ratner; Yoel Shoshan; Moshe Raboh; Sagi Polaczek; Michal Ozery-Flato; Daniel Shats; Liam Hazan; Sivan Ravid; Efrat Hexter (CoRR, 2024)

MAMMAL - Molecular Aligned Multi-Modal Architecture and Language. - Yoel Shoshan; Moshiko Raboh; Michal Ozery-Flato; Vadim Ratner; Alex Golts; Jeffrey K. Weber; Ella Barkan; Simona Rabinovici-Cohen; Sagi Polaczek; Ido Amos; Ben Shapira; Liam Hazan; et al. (CoRR, 2024)

Leveraging Large Language Models to Predict Antibody Biological Activity Against Influenza A Hemagglutinin. - Ella Barkan; Ibrahim Siddiqui; Kevin J. Cheng; Alex Golts; Yoel Shoshan; Jeffrey K. Weber; Yailin Campos Mota; Michal Ozery-Flato; Giuseppe A. Sautto (CoRR, 2025)