Predicting Generalization of AI Colonoscopy Models to Unseen Data

no code implementations • 14 Mar 2024 • Joel Shor, Carson McNeil, Yotam Intrator, Joseph R Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg

We test MSN's ability to be trained on data only from Israel and detect unseen techniques, narrow-band imaging (NBI) and chromendoscoy (CE), on colonoscopes from Japan (354 videos, 128 hours).

Breaking the Language Barrier: Can Direct Inference Outperform Pre-Translation in Multilingual LLM Applications?

no code implementations • 4 Mar 2024 • Yotam Intrator, Matan Halfon, Roman Goldenberg, Reut Tsarfaty, Matan Eyal, Ehud Rivlin, Yossi Matias, Natalia Aizenberg

Large language models hold significant promise in multilingual applications.

Translation

Self-Supervised Polyp Re-Identification in Colonoscopy

no code implementations • 14 Jun 2023 • Yotam Intrator, Natalie Aizenberg, Amir Livne, Ehud Rivlin, Roman Goldenberg

Computer-aided polyp detection (CADe) is becoming a standard, integral part of any modern colonoscopy system.

MDGAN: Boosting Anomaly Detection Using \\Multi-Discriminator Generative Adversarial Networks

no code implementations • 11 Oct 2018 • Yotam Intrator, Gilad Katz, Asaf Shabtai

Anomaly detection is often considered a challenging field of machine learning due to the difficulty of obtaining anomalous samples for training and the need to obtain a sufficient amount of training data.

Anomaly Detection valid