# Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures

arXiv: https://arxiv.org/abs/2102.12218 · HTML: https://mlsanity.com/paper/2102.12218/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 24 Feb 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture](https://mlsanity.com/paper/1411.4734/) (2014)
- [MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/1903.01945/) (2019)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [Temporal Convolutional Networks: A Unified Approach to Action Segmentation](https://mlsanity.com/paper/1608.08242/) (2016)
- [TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks](https://mlsanity.com/paper/2003.10751/) (2020)
- [Surgical Data Science: Enabling Next-Generation Surgery](https://mlsanity.com/paper/1701.06482/) (2017)
- [Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis](https://mlsanity.com/paper/1907.06099/) (2019)
- [Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks](https://mlsanity.com/paper/1802.08774/) (2018)
- [DeepPhase: Surgical Phase Recognition in CATARACTS Videos](https://mlsanity.com/paper/1807.10565/) (2018)
- [CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions](https://mlsanity.com/paper/1910.09031/) (2019)
- [Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video](https://mlsanity.com/paper/1907.11454/) (2019)
- [Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos](https://mlsanity.com/paper/1812.01366/) (2018)
- [Learning from a tiny dataset of manual annotations: a teacher/student approach for surgical phase recognition](https://mlsanity.com/paper/1812.00033/) (2018)

## Built on by (most cited first, 30 of 42)

- [Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos](https://mlsanity.com/paper/2109.03223/) (2021)
- [CholecTriplet2021: A benchmark challenge for surgical action triplet recognition](https://mlsanity.com/paper/2204.04746/) (2022)
- [Challenges in Multi-centric Generalization: Phase and Step Recognition in Roux-en-Y Gastric Bypass Surgery](https://mlsanity.com/paper/2312.11250/) (2023)
- [Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation](https://mlsanity.com/paper/2410.00263/) (2024)
- [Latent Graph Representations for Critical View of Safety Assessment](https://mlsanity.com/paper/2212.04155/) (2022)
- [Rendezvous in Time: An Attention-based Temporal Fusion approach for Surgical Triplet Recognition](https://mlsanity.com/paper/2211.16963/) (2022)
- [CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection](https://mlsanity.com/paper/2302.06294/) (2023)
- [Not End-to-End: Explore Multi-Stage Architecture for Online Surgical Phase Recognition](https://mlsanity.com/paper/2107.04810/) (2021)
- [Video-based fully automatic assessment of open surgery suturing skills](https://mlsanity.com/paper/2110.13972/) (2021)
- [VidLPRO: A underlineVideo-underlineLanguage underlinePre-training Framework for underlineRobotic and Laparoscopic Surgery](https://mlsanity.com/paper/2409.04732/) (2024)
- [Self-distillation for surgical action recognition](https://mlsanity.com/paper/2303.12915/) (2023)
- [Preserving Privacy in Surgical Video Analysis Using Artificial Intelligence: A Deep Learning Classifier to Identify Out-of-Body Scenes in Endoscopic Videos](https://mlsanity.com/paper/2301.07053/) (2023)
- [Instrument-tissue Interaction Detection Framework for Surgical Video Understanding](https://mlsanity.com/paper/2404.00322/) (2024)
- [TUNeS: A Temporal U-Net with Self-Attention for Video-based Surgical Phase Recognition](https://mlsanity.com/paper/2307.09997/) (2023)
- [SR-Mamba: Effective Surgical Phase Recognition with State Space Model](https://mlsanity.com/paper/2407.08333/) (2024)
- [Bounded Future MS-TCN++ for surgical gesture recognition](https://mlsanity.com/paper/2209.14647/) (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)
- [MURPHY: Relations Matter in Surgical Workflow Analysis](https://mlsanity.com/paper/2212.12719/) (2022)
- [CycleSAM: Few-Shot Surgical Scene Segmentation with Cycle- and Scene-Consistent Feature Matching](https://mlsanity.com/paper/2407.06795/) (2024)
- [Holistic Surgical Phase Recognition with Hierarchical Input Dependent State Space Models](https://mlsanity.com/paper/2506.21330/) (2025)
- [Adaptation of Multi-modal Representation Models for Multi-task Surgical Computer Vision](https://mlsanity.com/paper/2507.05020/) (2025)
- [Live Laparoscopic Video Retrieval with Compressed Uncertainty](https://mlsanity.com/paper/2203.04301/) (2022)
- [PoCaP Corpus: A Multimodal Dataset for Smart Operating Room Speech Assistant using Interventional Radiology Workflow Analysis](https://mlsanity.com/paper/2206.12320/) (2022)
- [TEMSET-24K: Densely Annotated Dataset for Indexing Multipart Endoscopic Videos using Surgical Timeline Segmentation](https://mlsanity.com/paper/2502.06708/) (2025)
- [Hierarchical Context Transformer for Multi-level Semantic Scene Understanding](https://mlsanity.com/paper/2502.15184/) (2025)
- [Predicting the Timing of Camera Movements From the Kinematics of Instruments in Robotic-Assisted Surgery Using Artificial Neural Networks](https://mlsanity.com/paper/2109.11192/) (2021)
- [Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits](https://mlsanity.com/paper/2306.16879/) (2023)
- [A Temporal Convolutional Network-Based Approach and a Benchmark Dataset for Colonoscopy Video Temporal Segmentation](https://mlsanity.com/paper/2502.03430/) (2025)
- [EndoARSS: Adapting Spatially-Aware Foundation Model for Efficient Activity Recognition and Semantic Segmentation in Endoscopic Surgery](https://mlsanity.com/paper/2506.06830/) (2025)

## Abstract (arXiv)

Purpose: Automatic segmentation and classification of surgical activity is crucial for providing advanced support in computer-assisted interventions and autonomous functionalities in robot-assisted surgeries. Prior works have focused on recognizing either coarse activities, such as phases, or fine-grained activities, such as gestures. This work aims at jointly recognizing two complementary levels of granularity directly from videos, namely phases and steps. Method: We introduce two correlated surgical activities, phases and steps, for the laparoscopic gastric bypass procedure. We propose a Multi-task Multi-Stage Temporal Convolutional Network (MTMS-TCN) along with a multi-task Convolutional Neural Network (CNN) training setup to jointly predict the phases and steps and benefit from their complementarity to better evaluate the execution of the procedure. We evaluate the proposed method on a large video dataset consisting of 40 surgical procedures (Bypass40). Results: We present experimental results from several baseline models for both phase and step recognition on the Bypass40 dataset. The proposed MTMS-TCN method outperforms in both phase and step recognition by 1-2% in accuracy, precision and recall, compared to single-task methods. Furthermore, for step recognition, MTMS-TCN achieves a superior performance of 3-6% compared to LSTM based models in accuracy, precision, and recall. Conclusion: In this work, we present a multi-task multi-stage temporal convolutional network for surgical activity recognition, which shows improved results compared to single-task models on the Bypass40 gastric bypass dataset with multi-level annotations. The proposed method shows that the joint modeling of phases and steps is beneficial to improve the overall recognition of each type of activity.