Latent Graph Representations for Critical View of Safety Assessment
ICube（ICube实验室）；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）；Institute of Image-Guided Surgery（图像引导外科研究所）；Fondazione Policlinico Universitario Agostino Gemelli IRCCS（阿戈斯蒂诺·杰梅里大学综合诊所基金会IRCCS）；Institute for Research against Digestive Cancer (IRCAD)（消化道癌症研究所（IRCAD））
Aditya Murali, Deepak Alapatt, Pietro Mascagni, Armine Vardazaryan, Alain Garcia, Nariaki Okamoto, Didier Mutter, Nicolas Padoy
Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their exposure. Prior works have approached this task by including semantic segmentation as an intermediate step, using predicted segmentation masks to then predict the CVS. While these methods are effective, they rely on extremely expensive ground-truth segmentation annotations and tend to fail when the predicted segmentation is incorrect, limiting generalization. In this work, we propose a method for CVS prediction wherein we first represent a surgical image using a disentangled latent scene graph, then process this representation using a graph neural network. Our graph representations explicitly encode semantic information - object location, class information, geometric relations - to improve anatomy-driven reasoning, as well as visual features to retain differentiability and thereby provide robustness to semantic errors. Finally, to address annotation cost, we propose to train our method using only bounding box annotations, incorporating an auxiliary image reconstruction objective to learn fine-grained object boundaries. We show that our method not only outperforms several baseline methods when trained with bounding box annotations, but also scales effectively when trained with segmentation masks, maintaining state-of-the-art performance.
The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment: Official Splits and Benchmark
ICube, University of Strasbourg, CNRS（ICube，斯特拉斯堡大学，CNRS）；IHU Strasbourg（斯特拉斯堡IHU）；Institute for Research against Digestive Cancer (IRCAD)（消化道癌症研究所）；Fondazione Policlinico Universitario A. Gemelli IRCCS（A.Gemelli大学附属医院）
University of Kentucky（肯塔基大学）；Kentucky Transportation Center（肯塔基交通中心）
This paper addresses the task of road safety assessment. An emerging approach for conducting such assessments in the United States is through the US Road Assessment Program (usRAP), which rates roads from highest risk (1 star) to lowest (5 stars). Obtaining these ratings requires manual, fine-grained labeling of roadway features in street-level panoramas, a slow and costly process. We propose to automate this process using a deep convolutional neural network that directly estimates the star rating from a street-level panorama, requiring milliseconds per image at test time. Our network also est…
University of Florence（佛罗伦萨大学）；Columbia University（哥伦比亚大学）
In this paper we present a machine vision system to efficiently monitor, analyze and present visual data acquired with a railway overhead gantry equipped with multiple cameras. This solution aims to improve the safety of daily life railway transportation in a two- fold manner: (1) by providing automatic algorithms that can process large imagery of trains (2) by helping train operators to keep attention on any possible malfunction. The system is designed with the latest cutting edge, high-rate visible and thermal cameras that ob- serve a train passing under an railway overhead gantry. The machi…
Multimodal Benchmark for Safety Assessment in Industrial Inspection Scenarios
Department of Automation, Tsinghua University（清华大学自动化系）；Institute for Embodied Intelligence and Robotics, Tsinghua University（清华大学具身智能与机器人研究所）；TetraBOT Intelligence Co., Ltd.（天博智能科技有限公司）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；School of Automation, Southeast University（东南大学自动化学院）
University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；Fondazione Policlinico Universitario A. Gemelli IRCCS（杰梅利大学医院基金会）；IHU-Strasbourg, Institute of Image-Guided Surgery（斯特拉斯堡大学医院-图像引导手术研究所）；Institute for Research against Digestive Cancer (IRCAD)（消化癌症研究所）
Minimally invasive image-guided surgery heavily relies on vision. Deep learning models for surgical video analysis could therefore support visual tasks such as assessing the critical view of safety (CVS) in laparoscopic cholecystectomy (LC), potentially contributing to surgical safety and efficiency. However, the performance, reliability and reproducibility of such models are deeply dependent on the quality of data and annotations used in their development. Here, we present a protocol, checklists, and visual examples to promote consistent annotation of hepatocystic anatomy and CVS criteria. We…
TSHA: A Benchmark for Visual Language Models in Trustworthy Safety Hazard Assessment Scenarios
City University of Hong Kong（香港城市大学）；East China Normal University（华东师范大学）；University of Western Australia（西澳大学）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Development Center of Computer Software Technology（上海计算机软件技术开发中心）；Zhejiang Gongshang University（浙江工商大学）
Safety Without Semantic Disruptions: Editing-free Safe Image Generation via Context-preserving Dual Latent Reconstruction
University of Western Australia（西澳大学）；University of Melbourne（墨尔本大学）；University of Central Florida（中佛罗里达大学）；Australian National University（澳大利亚国立大学）；Google（谷歌）
Clemson University（克莱姆森大学）；Northern Arizona University（北亚利桑那大学）；The University of Arizona（亚利桑那大学）；Arizona Department of Transportation（亚利桑那州交通局）；Exponent Co（Exponent公司）
Driving safety analysis has recently experienced unprecedented improvements thanks to technological advances in precise positioning sensors, artificial intelligence (AI)-based safety features, autonomous driving systems, connected vehicles, high-throughput computing, and edge computing servers. Particularly, deep learning (DL) methods empowered volume video processing to extract safety-related features from massive videos captured by roadside units (RSU). Safety metrics are commonly used measures to investigate crashes and near-conflict events. However, these metrics provide limited insight in…
Self-supervised large-scale kidney abnormality detection in drug safety assessment studies
Department of Pathology, Radboud University Medical Center（拉德堡德大学医学中心病理科部）；Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València（人类中心技术大学研究机构，巴塞罗那理工大学）
