106 citations · 150 across the 13 of their papers we have counts for
3 papers · 1 filter
Multi-Modal Self-Supervised Learning for Surgical Feedback Effectiveness Assessment
Arushi Gupta, Rafal Kocielnik, Jiayun Wang +5
During surgical training, real-time feedback from trainers to trainees is important for preventing errors and enhancing long-term skill acquisition. Accurately predicting the effec…
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
Hyewon Jeong, Sarah Jabbour, Yuzhe Yang +40
The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA. The symposium included research roundtable sessions to foster discussions between…
Deep Multimodal Fusion for Surgical Feedback Classification
Rafal Kocielnik, Elyssa Y. Wong, Timothy N. Chu +5
Quantification of real-time informal feedback delivered by an experienced surgeon to a trainee during surgery is important for skill improvements in surgical training. Such feedbac…