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.
Abstract (translated)
微创手术可以从自动化的外科工具检测中获益匪浅，这能够实现高级分析和辅助。然而，在手术环境中注释数据的有限可用性给训练健壮的深度学习模型带来了挑战。本文提出了一种新颖的分阶段自适应微调方法，该方法由两个步骤组成：线性探测阶段，用于在预训练的基于CNN（卷积神经网络）架构上附加分类层，并且一个渐进式冻结阶段，旨在动态减少可精调的层数，以调节对手术领域的适应。这种策略减少了网络复杂度并提高了效率，只需要一次训练循环即可完成，消除了多轮迭代的需求。 我们在Cholec80数据集上验证了我们的方法，该数据集中使用的是预训练于ImageNet上的CNN架构（如ResNet-50和DenseNet-121），用于在胆囊内镜视频中检测手术工具。我们的结果显示，相较于现有方法及传统的微调技术，本方法在检测性能方面有所提升，并且达到了96.4%的平均精度均值(mAP)。 为了评估其更广泛的应用性，我们还在CATARACTS数据集上进一步验证了微调策略的泛化能力。CATARACTS是一个完全不同的领域，涉及微创眼科手术。这些发现表明，渐进式冻结微调技术在不同类型的外科手术中对工具存在检测改进方面具有潜力，并可能在更广泛的图像分类任务中得到应用。
URL
https://arxiv.org/abs/2510.15372
