Tamajit Banerjee
Papers
1
Total Citations
3
H-Index
1
About
Tamajit Banerjee is a researcher at the intersection of computer vision, machine learning, and medical simulation, with a primary focus on automated skill assessment in surgical training. His most-cited work, "Representation Learning Using Rank Loss for Robust Neurosurgical Skills Evaluation" (2022), introduces a novel approach to evaluating trainee doctors' psychomotor skills from video recordings of simulated procedures. By employing a rank loss-based representation learning framework, Banerjee addresses the critical challenge of robust, objective skill evaluation—a key bottleneck for maximizing the utility of surgical simulators in medical education. His contributions aim to replace subjective, time-consuming human assessment with data-driven, automated methods that can scale across training programs. With 3 citations on this foundational paper, his work is gaining traction in the growing field of surgical data science. Banerjee’s research holds promise for improving the efficiency and consistency of neurosurgical training, ultimately enhancing patient safety by ensuring that trainees achieve proficiency before entering the operating room.
Research Focus
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Top Papers
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