# MIcro-Surgical Anastomose Workflow recognition challenge report

arXiv: https://arxiv.org/abs/2103.13111 · HTML: https://mlsanity.com/paper/2103.13111/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 24 Mar 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [Lookahead Optimizer: k steps forward, 1 step back](https://mlsanity.com/paper/1907.08610/) (2019)
- [Why rankings of biomedical image analysis competitions should be interpreted with care](https://mlsanity.com/paper/1806.02051/) (2018)
- [Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation](https://mlsanity.com/paper/1602.02995/) (2016)
- [Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis](https://mlsanity.com/paper/1907.06099/) (2019)
- [Methods and open-source toolkit for analyzing and visualizing challenge results](https://mlsanity.com/paper/1910.05121/) (2019)
- [Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video](https://mlsanity.com/paper/1907.11454/) (2019)
- [Recognizing Surgical Activities with Recurrent Neural Networks](https://mlsanity.com/paper/1606.06329/) (2016)
- [Evaluating surgical skills from kinematic data using convolutional neural networks](https://mlsanity.com/paper/1806.02750/) (2018)
- [Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery](https://mlsanity.com/paper/2011.01619/) (2020)
- [Offline identification of surgical deviations in laparoscopic rectopexy](https://mlsanity.com/paper/1909.10790/) (2019)
- [Surgical Gesture Recognition with Optical Flow only](https://mlsanity.com/paper/1904.01143/) (2019)
- [Automated Surgical Activity Recognition with One Labeled Sequence](https://mlsanity.com/paper/1907.08825/) (2019)

## Built on by (most cited first, 28 of 28)

- [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)
- [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)
- [A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action Recognition](https://mlsanity.com/paper/2303.13505/) (2023)
- [Less is More: Surgical Phase Recognition from Timestamp Supervision](https://mlsanity.com/paper/2202.08199/) (2022)
- [Instrument-tissue Interaction Detection Framework for Surgical Video Understanding](https://mlsanity.com/paper/2404.00322/) (2024)
- [MuST: Multi-Scale Transformers for Surgical Phase Recognition](https://mlsanity.com/paper/2407.17361/) (2024)
- [Integrating Artificial Intelligence and Augmented Reality in Robotic Surgery: An Initial dVRK Study Using a Surgical Education Scenario](https://mlsanity.com/paper/2201.00383/) (2022)
- [PEg TRAnsfer Workflow recognition challenge report: Does multi-modal data improve recognition?](https://mlsanity.com/paper/2202.05821/) (2022)
- [Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study](https://mlsanity.com/paper/2203.09230/) (2022)
- [Multitask Learning in Minimally Invasive Surgical Vision: A Review](https://mlsanity.com/paper/2401.08256/) (2024)
- [CPR-Coach: Recognizing Composite Error Actions based on Single-class Training](https://mlsanity.com/paper/2309.11718/) (2023)
- [ProstaTD: Bridging Surgical Triplet from Classification to Fully Supervised Detection](https://mlsanity.com/paper/2506.01130/) (2025)
- [Multimodal Graph Representation Learning for Robust Surgical Workflow Recognition with Adversarial Feature Disentanglement](https://mlsanity.com/paper/2505.01766/) (2025)
- [SurgMLLMBench: A Multimodal Large Language Model Benchmark Dataset for Surgical Scene Understanding](https://mlsanity.com/paper/2511.21339/) (2025)
- [Artificial Intelligence for Biomedical Video Generation](https://mlsanity.com/paper/2411.07619/) (2024)
- [Hierarchical Context Transformer for Multi-level Semantic Scene Understanding](https://mlsanity.com/paper/2502.15184/) (2025)
- [Towards Next-Generation Healthcare: A Survey of Medical Embodied AI for Perception, Decision-Making, and Action](https://mlsanity.com/paper/2606.15647/) (2026)
- [Human Gaze Guided Attention for Surgical Activity Recognition](https://mlsanity.com/paper/2203.04752/) (2022)
- [Surgical Video Understanding with Label Interpolation](https://mlsanity.com/paper/2509.18802/) (2025)
- [MiDAS: A Multimodal Data Acquisition System and Dataset for Robot-Assisted Minimally Invasive Surgery](https://mlsanity.com/paper/2602.12407/) (2026)
- [Microsurgical Instrument Segmentation for Robot-Assisted Surgery](https://mlsanity.com/paper/2509.11727/) (2025)
- [State-Change Learning for Prediction of Future Events in Endoscopic Videos](https://mlsanity.com/paper/2510.12904/) (2025)
- [Chain-of-Adaptation: Surgical Vision-Language Adaptation with Reinforcement Learning](https://mlsanity.com/paper/2603.20116/) (2026)
- [Temporally Consistent Label Interpolation for Robust Surgical Multi-Task Learning under Challenging Conditions](https://mlsanity.com/paper/2606.26634/) (2026)
- [SPIRIT: Spatio-temporal Pairwise Relational Modeling of Instrument-Tissue Interactions for Surgical Action Triplet Recognition](https://mlsanity.com/paper/2608.02188/) (2026)
- [Beyond Instrument Motion: Recognizing Tissue Tension Toward Surgical Skill Assessment](https://mlsanity.com/paper/2608.17935/) (2026)
- [Multi-modal Representation Fusion for Long-term Microsurgical Anastomosis Trajectory Anticipation](https://mlsanity.com/paper/2610.14492/) (2026)

## Abstract (arXiv)

The "MIcro-Surgical Anastomose Workflow recognition on training sessions" (MISAW) challenge provided a data set of 27 sequences of micro-surgical anastomosis on artificial blood vessels. This data set was composed of videos, kinematics, and workflow annotations described at three different granularity levels: phase, step, and activity. The participants were given the option to use kinematic data and videos to develop workflow recognition models. Four tasks were proposed to the participants: three of them were related to the recognition of surgical workflow at three different granularity levels, while the last one addressed the recognition of all granularity levels in the same model. One ranking was made for each task. We used the average application-dependent balanced accuracy (AD-Accuracy) as the evaluation metric. This takes unbalanced classes into account and it is more clinically relevant than a frame-by-frame score. Six teams, including a non-competing team, participated in at least one task. All models employed deep learning models, such as CNN or RNN. The best models achieved more than 95% AD-Accuracy for phase recognition, 80% for step recognition, 60% for activity recognition, and 75% for all granularity levels. For high levels of granularity (i.e., phases and steps), the best models had a recognition rate that may be sufficient for applications such as prediction of remaining surgical time or resource management. However, for activities, the recognition rate was still low for applications that can be employed clinically. The MISAW data set is publicly available to encourage further research in surgical workflow recognition. It can be found at www.synapse.org/MISAW