Deep Attention Based Semi-Supervised 2D-Pose Estimation for Surgical Instruments

TL;DR

This paper explores semi-supervised learning methods for 2D surgical instrument pose estimation, demonstrating significant improvements in accuracy on unseen geometries and achieving state-of-the-art results with a lightweight network.

Contribution

It introduces a lightweight network architecture and evaluates semi-supervised algorithms, including a novel confidence measure for pseudo-labeling, for 2D pose estimation of surgical instruments.

Findings

Semi-supervised learning drastically improves unseen geometry performance.

The proposed architecture outperforms state-of-the-art with supervised learning.

Pseudo-labeling achieves new state-of-the-art results on Endovis benchmark.

Abstract

For many practical problems and applications, it is not feasible to create a vast and accurately labeled dataset, which restricts the application of deep learning in many areas. Semi-supervised learning algorithms intend to improve performance by also leveraging unlabeled data. This is very valuable for 2D-pose estimation task where data labeling requires substantial time and is subject to noise. This work aims to investigate if semi-supervised learning techniques can achieve acceptable performance level that makes using these algorithms during training justifiable. To this end, a lightweight network architecture is introduced and mean teacher, virtual adversarial training and pseudo-labeling algorithms are evaluated on 2D-pose estimation for surgical instruments. For the applicability of pseudo-labelling algorithm, we propose a novel confidence measure, total variation. Experimental results show that utilization of semi-supervised learning improves the performance on unseen geometries drastically while maintaining high accuracy for seen geometries. For RMIT benchmark, our lightweight architecture outperforms state-of-the-art with supervised learning. For Endovis benchmark, pseudo-labelling algorithm improves the supervised baseline achieving the new state-of-the-art performance.