# MuST: Multi-Scale Transformers for Surgical Phase Recognition

arXiv: https://arxiv.org/abs/2407.17361 · HTML: https://mlsanity.com/paper/2407.17361/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 32 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://mlsanity.com/paper/2103.14899/) (2021)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [Anticipative Video Transformer](https://mlsanity.com/paper/2106.02036/) (2021)
- [TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks](https://mlsanity.com/paper/2003.10751/) (2020)
- [Surgical Data Science -- from Concepts toward Clinical Translation](https://mlsanity.com/paper/2011.02284/) (2020)
- [Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer](https://mlsanity.com/paper/2103.09712/) (2021)
- [AR-Net: Adaptive Frame Resolution for Efficient Action Recognition](https://mlsanity.com/paper/2007.15796/) (2020)
- [Temporal Memory Relation Network for Workflow Recognition from Surgical Video](https://mlsanity.com/paper/2103.16327/) (2021)
- [OperA: Attention-Regularized Transformers for Surgical Phase Recognition](https://mlsanity.com/paper/2103.03873/) (2021)
- [Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark](https://mlsanity.com/paper/2109.14956/) (2021)
- [LoViT: Long Video Transformer for Surgical Phase Recognition](https://mlsanity.com/paper/2305.08989/) (2023)
- [Pixel-Wise Recognition for Holistic Surgical Scene Understanding](https://mlsanity.com/paper/2401.11174/) (2024)
- [Towards Holistic Surgical Scene Understanding](https://mlsanity.com/paper/2212.04582/) (2022)
- [MIcro-Surgical Anastomose Workflow recognition challenge report](https://mlsanity.com/paper/2103.13111/) (2021)
- [Exploring Segment-level Semantics for Online Phase Recognition from Surgical Videos](https://mlsanity.com/paper/2111.11044/) (2021)
- [Single- and Multi-Task Architectures for Surgical Workflow Challenge at M2CAI 2016](https://mlsanity.com/paper/1610.08844/) (2016)
- [The Wisdom of Crowds: Temporal Progressive Attention for Early Action Prediction](https://mlsanity.com/paper/2204.13340/) (2022)

## Built on by (most cited first, 8 of 8)

- [SurgLaVi: Large-Scale Hierarchical Dataset for Surgical Vision-Language Representation Learning](https://mlsanity.com/paper/2509.10555/) (2025)
- [Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge](https://mlsanity.com/paper/2507.16559/) (2025)
- [Privacy-Preserving Operating Room Workflow Analysis using Digital Twins](https://mlsanity.com/paper/2504.12552/) (2025)
- [Holistic Surgical Phase Recognition with Hierarchical Input Dependent State Space Models](https://mlsanity.com/paper/2506.21330/) (2025)
- [SemiVT-Surge: Semi-Supervised Video Transformer for Surgical Phase Recognition](https://mlsanity.com/paper/2506.01471/) (2025)
- [HyperVLP: Enhancing Hierarchical Surgical Video-Language Pre-training in Hyperbolic Space](https://mlsanity.com/paper/2606.31245/) (2026)
- [Multi-scale Temporal Prediction via Incremental Generation and Multi-agent Collaboration](https://mlsanity.com/paper/2509.17429/) (2025)
- [OphBiWSSD: Scaling Temporal Action Localization in Ophthalmic Surgeries with Bidirectional Weight-tied State Space Duality](https://mlsanity.com/paper/2609.12409/) (2026)

## Abstract (arXiv)

Phase recognition in surgical videos is crucial for enhancing computer-aided surgical systems as it enables automated understanding of sequential procedural stages. Existing methods often rely on fixed temporal windows for video analysis to identify dynamic surgical phases. Thus, they struggle to simultaneously capture short-, mid-, and long-term information necessary to fully understand complex surgical procedures. To address these issues, we propose Multi-Scale Transformers for Surgical Phase Recognition (MuST), a novel Transformer-based approach that combines a Multi-Term Frame encoder with a Temporal Consistency Module to capture information across multiple temporal scales of a surgical video. Our Multi-Term Frame Encoder computes interdependencies across a hierarchy of temporal scales by sampling sequences at increasing strides around the frame of interest. Furthermore, we employ a long-term Transformer encoder over the frame embeddings to further enhance long-term reasoning. MuST achieves higher performance than previous state-of-the-art methods on three different public benchmarks.