# M2CAI Workflow Challenge: Convolutional Neural Networks with Time Smoothing and Hidden Markov Model for Video Frames Classification

arXiv: https://arxiv.org/abs/1610.05541 · HTML: https://mlsanity.com/paper/1610.05541/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2016.

## Built on by (most cited first, 6 of 6)

- [Temporal Memory Relation Network for Workflow Recognition from Surgical Video](https://mlsanity.com/paper/2103.16327/) (2021)
- [Neural Finite-State Machines for Surgical Phase Recognition](https://mlsanity.com/paper/2411.18018/) (2024)
- [Generalized Recognition of Basic Surgical Actions Enables Skill Assessment and Vision-Language-Model-based Surgical Planning](https://mlsanity.com/paper/2603.12787/) (2026)
- [Efficient Frame Extraction: A Novel Approach Through Frame Similarity and Surgical Tool Tracking for Video Segmentation](https://mlsanity.com/paper/2501.11153/) (2025)
- [MoSFormer: Augmenting Temporal Context with Memory of Surgery for Surgical Phase Recognition](https://mlsanity.com/paper/2503.00695/) (2025)
- [A Novel Deep ML Architecture by Integrating Visual Simultaneous Localization and Mapping (vSLAM) into Mask R-CNN for Real-time Surgical Video Analysis](https://mlsanity.com/paper/2103.16847/) (2021)

## Abstract (arXiv)

Our approach is among the three best to tackle the M2CAI Workflow challenge. The latter consists in recognizing the operation phase for each frames of endoscopic videos. In this technical report, we compare several classification models and temporal smoothing methods. Our submitted solution is a fine tuned Residual Network-200 on 80% of the training set with temporal smoothing using simple temporal averaging of the predictions and a Hidden Markov Model modeling the sequence.