Collated a list of useful open access work related to surgical phase recognition.

The following collection of content is divided into 6 parts:

• Surgical Artificial Intelligence

• Surgical phase recognition

• LSTM, RNN and VAE

• Transformers and Attention based mechanism

• Self-Supervised Learning

• Other Links

Papers review

• Gesture Recognition in Robotic Surgery: A Review, [Paper]

  • Beatrice van Amsterdam, Matthew J. Clarkson, Danail Stoyanov

• Cross-modal self-supervised representation learning for gesture and skill recognition in robotic surgery, MICCAI 2021, [Paper]

  • Jie Ying Wu, Aniruddha Tamhane, Peter Kazanzides & Mathias Unberath

• What is Artificial Intelligence Surgery?, [Paper]

  • Andrew A. Gumbs , Silvana Perretta , Bernard d’Allemagne , Elie Chouillard

• IRCAD online course - Artificial intelligence (AI) and surgery, [Paper]

• Surgical Data Science -- from Concepts to Clinical Translation, [Paper]
  • Lena Maier-Hein, Matthias Eisenmann, Duygu Sarikaya, Keno März, Toby Collins

• Artificial Intelligence in Surgery: Promises and Perils, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5995666/

• Surgical data science for next-generation interventions, 2017, https://www.researchgate.net/profile/Germain-Forestier/publication/319651707_Surgical_data_science_for_next-generation_interventions/links/5f6df6d092851c14bc94c81a/Surgical-data-science-for-next-generation-interventions.pdf

• Surgical data science: the new knowledge domain, 2017, https://www.degruyter.com/document/doi/10.1515/iss-2017-0004/html

Papers review

• Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer, MICCAI 2021, [Paper]

  • Xiaojie Gao, Yueming Jin

• OperA: Attention-Regularized Transformers for Surgical Phase Recognition, MICCAI 2021, [Paper]

  • Tobias Czempiel, Magdalini Paschali

• Aggregating Long-Term Context for Learning Laparoscopic and Robot-Assisted Surgical Workflows, https://arxiv.org/abs/2009.00681

• Against spatial–temporal discrepancy: contrastive learning-based network for surgical workflow recognition, IPCAI 2021, https://link.springer.com/article/10.1007%2Fs11548-021-02382-5

• Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures, IPCAI 2021, https://arxiv.org/abs/2102.12218

• SUrgical PRediction GAN for Events Anticipation, https://arxiv.org/abs/2105.04642

• Machine Learning for Surgical Phase Recognition A Systematic Review, https://journals.lww.com/annalsofsurgery/Fulltext/2021/04000/Machine_Learning_for_Surgical_Phase_Recognition__A.11.aspx

• Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach, https://link.springer.com/article/10.1007%2Fs00464-019-07281-0

• TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks, MICCAI 2020, https://arxiv.org/abs/2003.10751

• Assisted phase and step annotation for surgical videos, https://www.researchgate.net/publication/339158015_Assisted_phase_and_step_annotation_for_surgical_videos

• Impact of data on generalization of AI for surgical intelligence applications, https://arxiv.org/abs/1806.00466

• Aggregating Long-Term Context for Learning Laparoscopic and Robot-Assisted Surgical Workflows, https://arxiv.org/abs/2009.00681

• Towards Understanding Surgical Scenes Using Computer Vision - Bay Vision Virtual Meetup, https://www.youtube.com/watch?v=twy5ZG2VA_g

• MS-TCN: Multi-stage temporal convolutional network for action segmentation, CVPR 2019, https://arxiv.org/abs/1903.01945

• Learning from a tiny dataset of manual annotations: a teacher/student approach for surgical phase recognition, IPCAI 2019, https://arxiv.org/abs/1812.00033

• Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis, https://arxiv.org/abs/1907.06099

• Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video, MICCAI 2019, https://arxiv.org/abs/1907.11454

• Automated Surgical Activity Recognition with One Labeled Sequence, MICCAI 2019, https://arxiv.org/abs/1907.08825

• Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos, https://arxiv.org/abs/1812.01366

• Real-Time Extraction of Important Surgical Phases in Cataract Surgery Videos, https://www.nature.com/articles/s41598-019-53091-8

• Temporal coherence-based self-supervised learning for laparoscopic workflow analysis, Funke, Speidel, Bodenstedt, MICCAI 2018 https://arxiv.org/abs/1806.06811

• SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network, https://ieeexplore.ieee.org/document/8240734

• “Deep-Onto” network for surgical workflow and context recognition, https://link.springer.com/article/10.1007%2Fs11548-018-1882-8

• Surgical Activity Recognition in Robot-Assisted Radical Prostatectomy using Deep Learning, MICCAI 2018, https://arxiv.org/abs/1806.00466

• Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks, 2018, https://arxiv.org/abs/1805.08569

• DeepPhase: Surgical Phase Recognition in CATARACTS Videos, MICCAI 2018, https://arxiv.org/abs/1807.10565

• Tool and Phase recognition using contextual CNN features, 2016, https://arxiv.org/abs/1610.08854

• EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos, https://arxiv.org/abs/1602.03012

• Temporal Convolutional Networks: A Unified Approach to Action Segmentation, ECCV 2016, https://arxiv.org/abs/1608.08242

• LapOntoSPM: an ontology for laparoscopic surgeries and its application to surgical phase recognition, https://link.springer.com/article/10.1007/s11548-015-1222-1

• Surgical gesture classification from video and kinematic data, 2013, https://www.sciencedirect.com/science/article/abs/pii/S1361841513000522?via%3Dihub

Papers review

• Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures, https://scholar.google.co.uk/citations?hl=en&user=Gc2eg3kAAAAJ&view_op=list_works&sortby=pubdate

• A bio-inspired bistable recurrent cell allows for long-lasting memory, https://arxiv.org/abs/2006.05252

• Dynamical Variational Autoencoders: A Comprehensive Review, https://arxiv.org/abs/2008.12595

• Illustrated Guide to LSTM’s and GRU’s: A step by step explanation, 2018, https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21

• Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), 2017, https://www.youtube.com/watch?v=WCUNPb-5EYI

• LONG SHORT-TERM MEMORY, 1997, https://www.bioinf.jku.at/publications/older/2604.pdf

Papers review

• CLIP: Learning Transferable Visual Models From Natural Language Supervision, OpenAI 2021 https://arxiv.org/abs/2103.00020

• Perceiver: General Perception with Iterative Attention, DeepMind, ICML 2021, https://arxiv.org/abs/2103.03206

• Early Convolutions Help Transformers See Better, FAIR, https://arxiv.org/abs/2106.14881

• Trans-SVNet: Accurate Phase Recognition from Surgical Videos via Hybrid Embedding Aggregation Transformer, https://arxiv.org/abs/2103.09712

• Video Transformer Network, Theator, https://arxiv.org/abs/2102.00719

• When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations, https://arxiv.org/abs/2106.01548

• DINO: Emerging Properties in Self-Supervised Vision Transformers, FAIR, https://arxiv.org/abs/2104.14294

• ViT: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, https://arxiv.org/abs/2010.11929

• End-to-End Object Detection with Transformers, https://arxiv.org/abs/2005.12872

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2018, https://arxiv.org/abs/1810.04805

• Transformer: Attention Is All You Need, 2017, https://arxiv.org/abs/1706.03762

• SEER: Self-supervised Pretraining of Visual Features in the Wild, Goyal, Caron, Misra, https://arxiv.org/abs/2103.01988

• Barlow Twins: Self-Supervised Learning via Redundancy Reduction, Zbontar, LeCun, ICML 2021, https://arxiv.org/abs/2103.03230

• SwAV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, Caron, Misra, NIPS 2020, https://arxiv.org/abs/2006.09882

• Improved Baselines with Momentum Contrastive Learning, Chen, Fan, https://arxiv.org/abs/2003.04297

• SimCLR: A Simple Framework for Contrastive Learning, Chen, Hinton, ICML 2020, https://arxiv.org/abs/2002.05709

• MoCo: Momentum Contrast for Unsupervised Visual Representation Learning, CVPR 2020, https://arxiv.org/abs/1911.05722

Papers review

• MLP-Mixer: An all-MLP Architecture for Vision, https://arxiv.org/abs/2105.01601

• Involution: Inverting the Inherence of Convolution for Visual Recognition, https://arxiv.org/abs/2103.06255