Surgical phase recognition is a fundamental task in computer-assisted surgery systems. Most existing works are under the supervision of expensive and time-consuming full annotations, which require the surgeons to repeat watching videos to find the precise start and end time for a surgical phase. In this paper, we introduce timestamp supervision for surgical phase recognition to train the models with timestamp annotations, where the surgeons are asked to identify only a single timestamp within the temporal boundary of a phase. This annotation can significantly reduce the manual annotation cost compared to the full annotations. To make full use of such timestamp supervisions, we propose a novel method called uncertainty-aware temporal diffusion (UATD) to generate trustworthy pseudo labels for training. Our proposed UATD is motivated by the property of surgical videos, i.e., the phases are long events consisting of consecutive frames. To be specific, UATD diffuses the single labelled timestamp to its corresponding high confident ( i.e., low uncertainty) neighbour frames in an iterative way. Our study uncovers unique insights of surgical phase recognition with timestamp supervisions: 1) timestamp annotation can reduce 74% annotation time compared with the full annotation, and surgeons tend to annotate those timestamps near the middle of phases; 2) extensive experiments demonstrate that our method can achieve competitive results compared with full supervision methods, while reducing manual annotation cost; 3) less is more in surgical phase recognition, i.e., less but discriminative pseudo labels outperform full but containing ambiguous frames; 4) the proposed UATD can be used as a plug and play method to clean ambiguous labels near boundaries between phases, and improve the performance of the current surgical phase recognition methods.
Learning from Single Timestamps: Complexity Estimation in Laparoscopic Cholecystectomy
UCL Hawkes Institute, University College London（UCL哈维斯研究所，伦敦大学学院）；Medtronic plc.（美敦力公司）；Dept of Medical Physics & Biomedical Engineering, University College London（医学物理与生物医学工程系，伦敦大学学院）；Dept of Computer Science, University College London（计算机科学系，伦敦大学学院）
Free Lunch for Surgical Video Understanding by Distilling Self-Supervisions
The Hong Kong University of Science and Technology（香港科技大学）；Nanyang Technological University（南洋理工大学）；The Hong Kong University of Science and Technology Shenzhen Research Institute（香港科技大学深圳研究院）
Temporally Consistent Label Interpolation for Robust Surgical Multi-Task Learning under Challenging Conditions
Center for Humanoid Research, Korea Institute of Science and Technology（韩国科学技术研究院仿人机器人研究中心）；School of Electrical and Electronic Engineering, Yonsei University（延世大学电气与电子工程学院）
Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition
University of Verona（维罗纳大学）；University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；IHU Strasbourg（斯特拉斯堡大学医院研究所）；University Hospital of Strasbourg（斯特拉斯堡大学医院）；IRCAD（斯特拉斯堡微创外科研究中心）；Fondazione Policlinico Universitario Agostino Gemelli IRCCS（阿戈斯蒂诺·杰梅利大学医院基金会IRCCS）
Large-Small Model Collaboration for Zero-Shot Surgical Phase Recognition
The Chinese University of Hong Kong（香港中文大学）；The Hong Kong Polytechnic University（香港理工大学）；Institute of Medical Intelligence and XR, The Chinese University of Hong Kong（香港中文大学医学智能与XR研究所）
HecVL: Hierarchical Video-Language Pretraining for Zero-shot Surgical Phase Recognition
University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；ICube（ICube实验室）；IHU Strasbourg（斯特拉斯堡大学医院研究所）；Technische Universität München（慕尼黑工业大学）
Surgical Temporal Action-aware Network with Sequence Regularization for Phase Recognition
Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science&Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；Beijing Friendship Hospital, Capital Medical University（首都医科大学附属北京友谊医院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Wuhan AI Research（武汉人工智能研究院）；ObjectEye Inc.（奥博视科技公司）
LoViT: Long Video Transformer for Surgical Phase Recognition
Department of Surgical & Interventional Engineering, King’s College London（外科与介入工程系，伦敦国王学院）；Department of Peter Gorer（彼得·戈尔德系）；Department of Immunobiology, King’s College London（免疫生物学系，伦敦国王学院）
ProstaTD: Bridging Surgical Triplet from Classification to Fully Supervised Detection
School of Nursing, The Hong Kong Polytechnic University（香港理工大学护理学院）；Nanfang Hospital, Southern Medical University（南方医科大学南芳医院）；Department of Surgery, The Chinese University of Hong Kong（香港中文大学外科部）；South China University of Technology（华南理工大学）；Singapore Management University（新加坡国立管理学院）
