# Metrics Matter in Surgical Phase Recognition

arXiv: https://arxiv.org/abs/2305.13961 · HTML: https://mlsanity.com/paper/2305.13961/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 95 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 30 of 43)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Exact solutions to the nonlinear dynamics of learning in deep linear neural networks](https://mlsanity.com/paper/1312.6120/) (2013)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [Twins: Revisiting the Design of Spatial Attention in Vision Transformers](https://mlsanity.com/paper/2104.13840/) (2021)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Principal Neighbourhood Aggregation for Graph Nets](https://mlsanity.com/paper/2004.05718/) (2020)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [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)
- [Long-Term Feature Banks for Detailed Video Understanding](https://mlsanity.com/paper/1812.05038/) (2018)
- [Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates](https://mlsanity.com/paper/1708.07120/) (2017)
- [ASFormer: Transformer for Action Segmentation](https://mlsanity.com/paper/2110.08568/) (2021)
- [TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks](https://mlsanity.com/paper/2003.10751/) (2020)
- [CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80](https://mlsanity.com/paper/2012.12453/) (2020)
- [Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer](https://mlsanity.com/paper/2103.09712/) (2021)

## Built on by (most cited first, 14 of 14)

- [SurgLaVi: Large-Scale Hierarchical Dataset for Surgical Vision-Language Representation Learning](https://mlsanity.com/paper/2509.10555/) (2025)
- [TUNeS: A Temporal U-Net with Self-Attention for Video-based Surgical Phase Recognition](https://mlsanity.com/paper/2307.09997/) (2023)
- [SF-TMN: SlowFast Temporal Modeling Network for Surgical Phase Recognition](https://mlsanity.com/paper/2306.08859/) (2023)
- [Neural Finite-State Machines for Surgical Phase Recognition](https://mlsanity.com/paper/2411.18018/) (2024)
- [CAT-SG: A Large Dynamic Scene Graph Dataset for Fine-Grained Understanding of Cataract Surgery](https://mlsanity.com/paper/2506.21813/) (2025)
- [Federated EndoViT: Pretraining Vision Transformers via Federated Learning on Endoscopic Image Collections](https://mlsanity.com/paper/2504.16612/) (2025)
- [Benchmarking and Enhancing Surgical Phase Recognition Models for Robotic-Assisted Esophagectomy](https://mlsanity.com/paper/2412.04039/) (2024)
- [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)
- [SemiVT-Surge: Semi-Supervised Video Transformer for Surgical Phase Recognition](https://mlsanity.com/paper/2506.01471/) (2025)
- [Surgical Phase and Instrument Recognition: How to identify appropriate Dataset Splits](https://mlsanity.com/paper/2306.16879/) (2023)
- [ReSW-VL: Representation Learning for Surgical Workflow Analysis Using Vision-Language Model](https://mlsanity.com/paper/2505.13746/) (2025)
- [SurgicalMamba: Dual-Path SSD with State Regramming for Online Surgical Phase Recognition](https://mlsanity.com/paper/2605.14889/) (2026)
- [HTT-Net: Hierarchical Text-guided Transition Modeling for Surgical Video Phase Recognition](https://mlsanity.com/paper/2607.16787/) (2026)

## Abstract (arXiv)

Surgical phase recognition is a basic component for different context-aware applications in computer- and robot-assisted surgery. In recent years, several methods for automatic surgical phase recognition have been proposed, showing promising results. However, a meaningful comparison of these methods is difficult due to differences in the evaluation process and incomplete reporting of evaluation details. In particular, the details of metric computation can vary widely between different studies. To raise awareness of potential inconsistencies, this paper summarizes common deviations in the evaluation of phase recognition algorithms on the Cholec80 benchmark. In addition, a structured overview of previously reported evaluation results on Cholec80 is provided, taking known differences in evaluation protocols into account. Greater attention to evaluation details could help achieve more consistent and comparable results on the surgical phase recognition task, leading to more reliable conclusions about advancements in the field and, finally, translation into clinical practice.