# Dual Invariance Self-training for Reliable Semi-supervised Surgical Phase Recognition

arXiv: https://arxiv.org/abs/2501.17628 · HTML: https://mlsanity.com/paper/2501.17628/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 19 references that are in the map.
- First posted on arXiv 29 Jan 2025.

## Builds on (most cited first, 13 of 13)

- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://mlsanity.com/paper/2106.01226/) (2021)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [Contrastive learning of global and local features for medical image segmentation with limited annotations](https://mlsanity.com/paper/2006.10511/) (2020)
- [ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation](https://mlsanity.com/paper/2106.05095/) (2021)
- [Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition](https://mlsanity.com/paper/1708.07632/) (2017)
- [SVFormer: Semi-supervised Video Transformer for Action Recognition](https://mlsanity.com/paper/2211.13222/) (2022)
- [Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition](https://mlsanity.com/paper/2112.09690/) (2021)
- [Learning from Temporal Gradient for Semi-supervised Action Recognition](https://mlsanity.com/paper/2111.13241/) (2021)
- [TimeBalance: Temporally-Invariant and Temporally-Distinctive Video Representations for Semi-Supervised Action Recognition](https://mlsanity.com/paper/2303.16268/) (2023)
- [Relevance Detection in Cataract Surgery Videos by Spatio-Temporal Action Localization](https://mlsanity.com/paper/2104.14280/) (2021)
- [Event Recognition in Laparoscopic Gynecology Videos with Hybrid Transformers](https://mlsanity.com/paper/2312.00593/) (2023)
- [Action Recognition in Video Recordings from Gynecologic Laparoscopy](https://mlsanity.com/paper/2311.18666/) (2023)

## Built on by (most cited first, 3 of 3)

- [WetCat: Enabling Automated Skill Assessment in Wet-Lab Cataract Surgery Videos](https://mlsanity.com/paper/2506.08896/) (2025)
- [Data-Efficient Learning for Generalizable Surgical Video Understanding](https://mlsanity.com/paper/2508.10215/) (2025)
- [SAM-Fed: SAM-Guided Federated Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2511.14302/) (2025)

## Abstract (arXiv)

Accurate surgical phase recognition is crucial for advancing computer-assisted interventions, yet the scarcity of labeled data hinders training reliable deep learning models. Semi-supervised learning (SSL), particularly with pseudo-labeling, shows promise over fully supervised methods but often lacks reliable pseudo-label assessment mechanisms. To address this gap, we propose a novel SSL framework, Dual Invariance Self-Training (DIST), that incorporates both Temporal and Transformation Invariance to enhance surgical phase recognition. Our two-step self-training process dynamically selects reliable pseudo-labels, ensuring robust pseudo-supervision. Our approach mitigates the risk of noisy pseudo-labels, steering decision boundaries toward true data distribution and improving generalization to unseen data. Evaluations on Cataract and Cholec80 datasets show our method outperforms state-of-the-art SSL approaches, consistently surpassing both supervised and SSL baselines across various network architectures.