Adaptive transfer learning for surgical tool presence detection in laparoscopic videos through gradual freezing fine-tuning

• URL: http://arxiv.org/abs/2510.15372v1
• Date: Fri, 17 Oct 2025 07:17:52 GMT
• Title: Adaptive transfer learning for surgical tool presence detection in laparoscopic videos through gradual freezing fine-tuning
• Authors: Ana Davila, Jacinto Colan, Yasuhisa Hasegawa,
• Abstract summary: Minimally invasive surgery can benefit from automated surgical tool detection, enabling advanced analysis and assistance.<n>The limited availability of annotated data in surgical settings poses a challenge for training robust deep learning models.<n>This paper introduces a novel staged adaptive fine-tuning approach consisting of two steps: a linear probing stage and a gradual freezing stage.
• Score: 1.1371756033920992
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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