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About

Avinash Kori is a leading researcher at the intersection of medical image analysis, computer vision, and deep learning, with a particular focus on surgical scene understanding and computational pathology. His most impactful work includes contributions to the 2018 Robotic Scene Segmentation Challenge, a landmark effort that established a standardized benchmark for instrument segmentation in robotic surgery. This challenge, initiated at the EndoVis workshop during MICCAI 2015, pioneered the use of automatically generated annotations from robot forward kinematics and CAD models, enabling large-scale, reproducible evaluation of segmentation algorithms. The resulting dataset and competition have garnered over 119 citations, becoming a foundational resource for the surgical vision community. Kori’s broader research advances explainable and efficient deep learning architectures for medical imaging, including novel approaches for domain adaptation, uncertainty estimation, and multi-modal learning in histopathology and endoscopy. His work is widely cited for bridging the gap between clinical needs and algorithmic innovation, with contributions that directly impact the robustness and interpretability of AI in real-world surgical and diagnostic settings.