# Surgical Visual Domain Adaptation: Results from the MICCAI 2020 SurgVisDom Challenge

arXiv: https://arxiv.org/abs/2102.13644 · HTML: https://mlsanity.com/paper/2102.13644/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 48 references that are in the map.
- First posted on arXiv 26 Feb 2021.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Lookahead Optimizer: k steps forward, 1 step back](https://mlsanity.com/paper/1907.08610/) (2019)
- [Large-scale weakly-supervised pre-training for video action recognition](https://mlsanity.com/paper/1905.00561/) (2019)
- [Surgical Data Science: Enabling Next-Generation Surgery](https://mlsanity.com/paper/1701.06482/) (2017)
- [Compressed Video Action Recognition](https://mlsanity.com/paper/1712.00636/) (2017)
- [TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation](https://mlsanity.com/paper/1801.05746/) (2018)
- [Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training](https://mlsanity.com/paper/1711.06606/) (2017)
- [BIAS: Transparent reporting of biomedical image analysis challenges](https://mlsanity.com/paper/1910.04071/) (2019)
- [DeepPhase: Surgical Phase Recognition in CATARACTS Videos](https://mlsanity.com/paper/1807.10565/) (2018)
- [Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video](https://mlsanity.com/paper/1907.11454/) (2019)
- [Automatic Operating Room Surgical Activity Recognition for Robot-Assisted Surgery](https://mlsanity.com/paper/2006.16166/) (2020)
- [Exploiting the potential of unlabeled endoscopic video data with self-supervised learning](https://mlsanity.com/paper/1711.09726/) (2017)
- [Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation](https://mlsanity.com/paper/2007.11514/) (2020)
- [Surgical Activity Recognition in Robot-Assisted Radical Prostatectomy using Deep Learning](https://mlsanity.com/paper/1806.00466/) (2018)
- [RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation Network](https://mlsanity.com/paper/1905.08663/) (2019)
- [Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos](https://mlsanity.com/paper/2006.00545/) (2020)
- [Tool and Phase recognition using contextual CNN features](https://mlsanity.com/paper/1610.08854/) (2016)
- [Novel evaluation of surgical activity recognition models using task-based efficiency metrics](https://mlsanity.com/paper/1907.02060/) (2019)

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

- [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)
- [Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report Generation](https://mlsanity.com/paper/2107.11091/) (2021)
- [Towards Low-Cost and Efficient Malaria Detection](https://mlsanity.com/paper/2111.13656/) (2021)
- [VidLPRO: A underlineVideo-underlineLanguage underlinePre-training Framework for underlineRobotic and Laparoscopic Surgery](https://mlsanity.com/paper/2409.04732/) (2024)
- [EndoOmni: Zero-Shot Cross-Dataset Depth Estimation in Endoscopy by Robust Self-Learning from Noisy Labels](https://mlsanity.com/paper/2409.05442/) (2024)
- [Surgical Visual Understanding (SurgVU) Dataset](https://mlsanity.com/paper/2501.09209/) (2025)
- [Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Review](https://mlsanity.com/paper/2502.14886/) (2025)
- [Objective Surgical Skills Assessment and Tool Localization: Results from the MICCAI 2021 SimSurgSkill Challenge](https://mlsanity.com/paper/2212.04448/) (2022)
- [StyleFlow For Content-Fixed Image to Image Translation](https://mlsanity.com/paper/2207.01909/) (2022)
- [Artificial Intelligence for Biomedical Video Generation](https://mlsanity.com/paper/2411.07619/) (2024)
- [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026)
- [Deep Homography Estimation in Dynamic Surgical Scenes for Laparoscopic Camera Motion Extraction](https://mlsanity.com/paper/2109.15098/) (2021)
- [Mending of Spatio-Temporal Dependencies in Block Adjacency Matrix](https://mlsanity.com/paper/2310.02606/) (2023)
- [CauCLIP: Bridging the Sim-to-Real Gap in Surgical Video Understanding via Causality-Inspired Vision-Language Modeling](https://mlsanity.com/paper/2602.06619/) (2026)

## Abstract (arXiv)

Surgical data science is revolutionizing minimally invasive surgery by enabling context-aware applications. However, many challenges exist around surgical data (and health data, more generally) needed to develop context-aware models. This work - presented as part of the Endoscopic Vision (EndoVis) challenge at the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2020 conference - seeks to explore the potential for visual domain adaptation in surgery to overcome data privacy concerns. In particular, we propose to use video from virtual reality (VR) simulations of surgical exercises in robotic-assisted surgery to develop algorithms to recognize tasks in a clinical-like setting. We present the performance of the different approaches to solve visual domain adaptation developed by challenge participants. Our analysis shows that the presented models were unable to learn meaningful motion based features form VR data alone, but did significantly better when small amount of clinical-like data was also made available. Based on these results, we discuss promising methods and further work to address the problem of visual domain adaptation in surgical data science. We also release the challenge dataset publicly at https://www.synapse.org/surgvisdom2020.