Jumpstarting Surgical Computer Vision
December 10, 2023 Β· Declared Dead Β· π International Conference on Medical Image Computing and Computer-Assisted Intervention
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Authors
Deepak Alapatt, Aditya Murali, Vinkle Srivastav, Pietro Mascagni, AI4SafeChole Consortium, Nicolas Padoy
arXiv ID
2312.05968
Category
cs.CV: Computer Vision
Citations
8
Venue
International Conference on Medical Image Computing and Computer-Assisted Intervention
Last Checked
6 months ago
Abstract
Consensus amongst researchers and industry points to a lack of large, representative annotated datasets as the biggest obstacle to progress in the field of surgical data science. Advances in Self-Supervised Learning (SSL) represent a solution, reducing the dependence on large labeled datasets by providing task-agnostic initializations. However, the robustness of current self-supervised learning methods to domain shifts remains unclear, limiting our understanding of its utility for leveraging diverse sources of surgical data. Shifting the focus from methods to data, we demonstrate that the downstream value of SSL-based initializations is intricately intertwined with the composition of pre-training datasets. These results underscore an important gap that needs to be filled as we scale self-supervised approaches toward building general-purpose "foundation models" that enable diverse use-cases within the surgical domain. Through several stages of controlled experimentation, we develop recommendations for pretraining dataset composition evidenced through over 300 experiments spanning 20 pre-training datasets, 9 surgical procedures, 7 centers (hospitals), 3 labeled-data settings, 3 downstream tasks, and multiple runs. Using the approaches here described, we outperform state-of-the-art pre-trainings on two public benchmarks for phase recognition: up to 2.2% on Cholec80 and 5.1% on AutoLaparo.
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