Adaptive Transfer Learning for Surgical Tool Presence Detection in Laparoscopic Videos Through Gradual Freezing Fine‐Tuning

Minimally invasive surgery can benefit significantly from automated surgical tool detection, enabling advanced analysis and assistance. However, the limited availability of annotated data in surgical settings poses a challenge for training robust deep learning models. This paper introduces a novel staged adaptive fine‐tuning approach consisting of two steps: a linear probing stage to condition additional classification layers on a pre‐trained CNN‐based architecture and a gradual freezing stage to dynamically reduce the fine‐tunable layers, aiming to regulate adaptation to the surgical domain. This strategy reduces network complexity and improves efficiency, requiring only a single training loop and eliminating the need for multiple iterations. We validated our method on the Cholec80 dataset, employing CNN architectures (ResNet‐50 and DenseNet‐121) pre‐trained on ImageNet for detecting surgical tools in cholecystectomy endoscopic videos. Our results demonstrate that our method improves detection performance compared to existing approaches and established fine‐tuning techniques, achieving a mean average precision (mAP) of 96.4%. To assess its broader applicability, the generalizability of the fine‐tuning strategy was further confirmed on the CATARACTS dataset, a distinct domain of minimally invasive ophthalmic surgery. These findings suggest that gradual freezing fine‐tuning is a promising technique for improving tool presence detection in diverse surgical procedures and may have broader applications in general image classification tasks.