# Single- and Multi-Task Architectures for Surgical Workflow Challenge at M2CAI 2016

arXiv: https://arxiv.org/abs/1610.08844 · HTML: https://mlsanity.com/paper/1610.08844/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2016.

## Built on by (most cited first, 19 of 19)

- [Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer](https://mlsanity.com/paper/2103.09712/) (2021)
- [Temporal Memory Relation Network for Workflow Recognition from Surgical Video](https://mlsanity.com/paper/2103.16327/) (2021)
- [LoViT: Long Video Transformer for Surgical Phase Recognition](https://mlsanity.com/paper/2305.08989/) (2023)
- [Surgformer: Surgical Transformer with Hierarchical Temporal Attention for Surgical Phase Recognition](https://mlsanity.com/paper/2408.03867/) (2024)
- [Exploring Segment-level Semantics for Online Phase Recognition from Surgical Videos](https://mlsanity.com/paper/2111.11044/) (2021)
- [Not End-to-End: Explore Multi-Stage Architecture for Online Surgical Phase Recognition](https://mlsanity.com/paper/2107.04810/) (2021)
- [EgoSurgery-Phase: A Dataset of Surgical Phase Recognition from Egocentric Open Surgery Videos](https://mlsanity.com/paper/2405.19644/) (2024)
- [Less is More: Surgical Phase Recognition from Timestamp Supervision](https://mlsanity.com/paper/2202.08199/) (2022)
- [MuST: Multi-Scale Transformers for Surgical Phase Recognition](https://mlsanity.com/paper/2407.17361/) (2024)
- [Towards Graph Representation Learning Based Surgical Workflow Anticipation](https://mlsanity.com/paper/2208.03824/) (2022)
- [Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition](https://mlsanity.com/paper/2302.10834/) (2023)
- [Multitask Learning in Minimally Invasive Surgical Vision: A Review](https://mlsanity.com/paper/2401.08256/) (2024)
- [SPRMamba: Surgical Phase Recognition for Endoscopic Submucosal Dissection with Mamba](https://mlsanity.com/paper/2409.12108/) (2024)
- [SURGENT: A Surgical Multi-Agent Assistance System Across the Perioperative Workflow](https://mlsanity.com/paper/2605.29368/) (2026)
- [ViTALS: Vision Transformer for Action Localization in Surgical Nephrectomy](https://mlsanity.com/paper/2405.02571/) (2024)
- [SurgPLAN++: Universal Surgical Phase Localization Network for Online and Offline Inference](https://mlsanity.com/paper/2409.12467/) (2024)
- [ArthroPhase: A Novel Dataset and Method for Phase Recognition in Arthroscopic Video](https://mlsanity.com/paper/2502.07431/) (2025)
- [Surgical Foundation Model Leveraging Compression and Entropy Maximization for Image-Guided Surgical Assistance](https://mlsanity.com/paper/2506.01980/) (2025)
- [SurgPhase: Time efficient pituitary tumor surgery phase recognition via an interactive web platform](https://mlsanity.com/paper/2603.24897/) (2026)

## Abstract (arXiv)

The surgical workflow challenge at M2CAI 2016 consists of identifying 8 surgical phases in cholecystectomy procedures. Here, we propose to use deep architectures that are based on our previous work where we presented several architectures to perform multiple recognition tasks on laparoscopic videos. In this technical report, we present the phase recognition results using two architectures: (1) a single-task architecture designed to perform solely the surgical phase recognition task and (2) a multi-task architecture designed to perform jointly phase recognition and tool presence detection. On top of these architectures we propose to use two different approaches to enforce the temporal constraints of the surgical workflow: (1) HMM-based and (2) LSTM-based pipelines. The results show that the LSTM-based approach is able to outperform the HMM-based approach and also to properly enforce the temporal constraints into the recognition process.