Promptable cancer segmentation using minimal expert-curated data
Department of Medical Physics and Biomedical Engineering, University College London（医学物理与生物医学工程系，伦敦大学学院）；UCL Hawkes Institute, University College London（UCL Hawkes研究所，伦敦大学学院）；Division of Surgery and Interventional Science, University College London（外科与介入科学系，伦敦大学学院）；Department of Radiology（放射科；泌尿科，斯坦福大学）；and Department of Urology, Stanford University
Automated segmentation of cancer on medical images can aid targeted diagnostic and therapeutic procedures. However, its adoption is limited by the high cost of expert annotations required for training and inter-observer variability in datasets. While weakly-supervised methods mitigate some challenges, using binary histology labels for training as opposed to requiring full segmentation, they require large paired datasets of histology and images, which are difficult to curate. Similarly, promptable segmentation aims to allow segmentation with no re-training for new tasks at inference, however, existing models perform poorly on pathological regions, again necessitating large datasets for training. In this work we propose a novel approach for promptable segmentation requiring only 24 fully-segmented images, supplemented by 8 weakly-labelled images, for training. Curating this minimal data to a high standard is relatively feasible and thus issues with the cost and variability of obtaining labels can be mitigated. By leveraging two classifiers, one weakly-supervised and one fully-supervised, our method refines segmentation through a guided search process initiated by a single-point prompt. Our approach outperforms existing promptable segmentation methods, and performs comparably with fully-supervised methods, for the task of prostate cancer segmentation, while using substantially less annotated data (up to 100X less). This enables promptable segmentation with very minimal labelled data, such that the labels can be curated to a very high standard.
Promptable segmentation with region exploration enables minimal-effort expert-level prostate cancer delineation
UCL Hawkes Institute（UCL Hawkes Institute；医学物理与生物医学工程系）；Department of Medical Physics and Biomedical Engineering（大学学院伦敦）；University College London（医学成像中心）；Centre of Medical Imaging（肿瘤学系）；Department of Oncology（阿迦·柯尔大学医院）；Aga Khan University Hospital（生物工程中心；工程与材料科学学院）；Centre for Bioengineering；School of Engineering and Materials Science
Breast cancer screening is one of the most common radiological tasks with over 39 million exams performed each year. While breast cancer screening has been one of the most studied medical imaging applications of artificial intelligence, the development and evaluation of the algorithms are hindered due to the lack of well-annotated large-scale publicly available datasets. This is particularly an issue for digital breast tomosynthesis (DBT) which is a relatively new breast cancer screening modality. We have curated and made publicly available a large-scale dataset of digital breast tomosynthesis…
A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation
School of Computer Science and Technology, Shandong University of Finance and Economics（山东财经大学计算机科学与技术学院）；School of Information Science and Technology, Linyi University（临沂大学信息科学与技术学院）；Thoracic Surgery Department of Linyi Central Hospital（临沂市中心医院胸外科）；Personnel Department of Linyi Central Hospital（临沂市中心医院人事科）
CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Hupan Lab（湖畔实验室）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）；Sichuan Cancer Hospital（四川省肿瘤医院）；The First Affiliated Hospital of Zhejiang University（浙江大学医学院附属第一医院）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）；Guangdong Provincial People’s Hospital（广东省人民医院）
FDG PET/CT imaging is a resource intensive examination critical for managing malignant disease and is particularly important for longitudinal assessment during therapy. Approaches to automate longtudinal analysis present many challenges including lack of available longitudinal datasets, managing complex large multimodal imaging examinations, and need for detailed annotations for traditional supervised machine learning. In this work we develop OncoNet, novel machine learning algorithm that assesses treatment response from a 1,954 pairs of sequential FDG PET/CT exams through weak supervision usi…
PETWB-REP: A Multi-Cancer Whole-Body FDG PET/CT and Radiology Report Dataset for Medical Imaging Research
PET Center, Huashan Hospital, Fudan University（复旦大学附属华山医院PET中心）；Shanghai Academy of Artificial Intelligence for Science（上海科学智能研究院）；Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（复旦大学类脑智能科学与技术研究院）；Shanghai Universal Medical Imaging Diagnostic Center（上海环球医学影像诊断中心）；Human Phenome Institute, Fudan University（复旦大学人类表型组研究院）；School of Data Science, Fudan University（复旦大学数据科学学院）
Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Kaiser Permanente Northern California, Division of Research（北加州凯撒永久医疗集团研究部）
A major limitation in applying deep learning to artificial intelligence (AI) systems is the scarcity of high-quality curated datasets. We investigate strong augmentation based self-supervised learning (SSL) techniques to address this problem. Using breast cancer detection as an example, we first identify a mammogram-specific transformation paradigm and then systematically compare four recent SSL methods representing a diversity of approaches. We develop a method to convert a pretrained model from making predictions on uniformly tiled patches to whole images, and an attention-based pooling meth…
Huron Digital Pathology（休伦数字病理学公司）；University of Waterloo（滑铁卢大学）；Vector Institute（矢量研究所）；McMaster University（麦克马斯特大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）
The emergence of digital pathology has opened new horizons for histopathology and cytology. Artificial-intelligence algorithms are able to operate on digitized slides to assist pathologists with diagnostic tasks. Whereas machine learning involving classification and segmentation methods have obvious benefits for image analysis in pathology, image search represents a fundamental shift in computational pathology. Matching the pathology of new patients with already diagnosed and curated cases offers pathologist a novel approach to improve diagnostic accuracy through visual inspection of similar c…
Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations
Brigham and Women's Hospital（布莱根妇女医院）；Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；PixelMed Publishing（PixelMed出版公司）；Massachusetts General Hospital（麻省总医院）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；Maastricht University（马斯特里赫特大学）
University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）；UNSW（新南威尔士大学）
Gigapixel medical images provide massive data, both morphological textures and spatial information, to be mined. Due to the large data scale in histology, deep learning methods play an increasingly significant role as feature extractors. Existing solutions heavily rely on convolutional neural networks (CNNs) for global pixel-level analysis, leaving the underlying local geometric structure such as the interaction between cells in the tumor microenvironment unexplored. The topological structure in medical images, as proven to be closely related to tumor evolution, can be well characterized by gr…
CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation
University of Science and Technology of China（中国科学技术大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）
本文构建像素级标注的大规模直肠癌 CT 数据集 CARE，并提出融合提示、卷积与跳跃连接的 U-SAM 分割模型，在 CARE 和 WORD 上均取得优于现有方法的表现。
Medical Image De-Identification Resources: Synthetic DICOM Data and Tools for Validation
University of Arkansas for Medical Sciences（亚拉巴马医学科学大学）；Frederick National Laboratory for Cancer Research（癌症研究国家实验室）；Ellumen, Inc.（Ellumen公司）；Deloitte Consulting LLP（德勤咨询有限公司）；National Cancer Institute（国家癌症研究所）；Pixelmed Publishing（Pixelmed出版公司）；National Heart, Lung, and Blood Institute（国家心脏、肺部和血液研究所）
Images remain the largest data source in the field of healthcare. But at the same time, they are the most difficult to analyze. More than often, these images are analyzed by human experts such as pathologists and physicians. But due to considerable variation in pathology and the potential fatigue of human experts, an automated solution is much needed. The recent advancement in Deep learning could help us achieve an efficient and economical solution for the same. In this research project, we focus on developing a Deep Learning-based solution for detecting Multiple Myeloma cancer cells using an …
The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography
Radboud University Medical Center（拉德堡德大学医学中心）；Jeroen Bosch Hospital（杰伦博世医院）；University Medical Center Groningen（格罗宁根大学医学中心）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学梅维斯研究所）
Glioma C6: A Novel Dataset for Training and Benchmarking Cell Segmentation
Pavlov Institute of Physiology, Russian academy of science（俄罗斯科学院帕夫洛夫生理研究所）；Saint-Petersburg State University of Aerospace Instrumentation（圣彼得堡航空航天仪器州大学）；Institute of Physiology, NAS of Belarus（白俄罗斯国家科学院生理研究所）
LesiOnTime -- Joint Temporal and Clinical Modeling for Small Breast Lesion Segmentation in Longitudinal DCE-MRI
Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria（医学影像研究实验室，生物医学影像与影像引导治疗系，维也纳医科大学）；Comprehensive Center for AI in Medicine, Medical University of Vienna, Austria（医学人工智能综合中心，维也纳医科大学）；Breast Health Center, Privatklinik Döbling, Austria（乳腺健康中心，多克林根私人诊所）；Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria（生物医学影像与影像引导治疗系，维也纳医科大学）；Department of Obstetrics and Gynecology, Medical University of Vienna, Austria（妇产科系，维也纳医科大学）；Department of Pathology, Medical University of Vienna, Austria（病理学系，维也纳医科大学）；Comprehensive Cancer Center, Medical University of Vienna, Austria（综合癌症中心，维也纳医科大学）
Specialty-Oriented Generalist Medical AI for Chest CT Screening
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Wake Forest University School of Medicine（维克森林大学医学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines
The Hospital for Sick Children (SickKids)（SickKids医院（病童医院））；SickKids Research Institute（SickKids研究所）；University of Toronto（多伦多大学）；University Hospital Augsburg（奥格斯堡大学医院）；Institute of Medical Science, University of Toronto（多伦多大学医学科学研究所）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Department of Mechanical and Industrial Engineering, University of Toronto（多伦多大学机械与工业工程系）；Vector Institute（向量研究院）
Google AI Healthcare（谷歌AI医疗保健）；Avoneaux Medical Institute（阿沃诺医疗研究所）；University of Tennessee Health Science Center（田纳西大学健康科学中心）
The increasing availability of large institutional and public histopathology image datasets is enabling the searching of these datasets for diagnosis, research, and education. Though these datasets typically have associated metadata such as diagnosis or clinical notes, even carefully curated datasets rarely contain annotations of the location of regions of interest on each image. Because pathology images are extremely large (up to 100,000 pixels in each dimension), further laborious visual search of each image may be needed to find the feature of interest. In this paper, we introduce a deep le…
A Multicentric Dataset for Training and Benchmarking Breast Cancer Segmentation in H&E Slides
Department of Pathology, Radboud University Medical Center（病理学系，拉德堡德大学医学中心）；Fondazione Bruno Kessler（布鲁诺·凯塞勒基金会）；Department of Cellular, Computational and Integrative Biology, University of Trento（细胞、计算与整合生物学系，特伦托大学）；Biopticka Laboratory Ltd.（Biopticka实验室有限公司）；Department of Pathology, Faculty of Medicine in Pilsen, Charles University（病理学系，皮尔森医学院，查尔斯大学）；University of Washington Medical Center（华盛顿大学医学中心）；Department of Surgical Pathology, Santa Chiara Hospital, APSS（外科病理学系，圣查理医院，APSS）；CISMed - Centre for Medical Sciences, University of Trento（CISMed医学研究中心，特伦托大学）；Pathology Unit, University and Hospital Trust of Verona（病理学部，韦罗纳大学与医院信托）；IRCCS Sacro Cuore Don Calabria Hospital（IRCCS圣心多玛利亚医院）
Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation
The Hong Kong University of Science and Technology（香港科技大学）；The Chinese University of Hong Kong（香港中文大学）；Tencent AI Lab（腾讯AI实验室）；The First Affiliated Hospital, Sun Yat-sen University（中山大学附属第一医院）；Peking University Shenzhen Hospital（北京大学深圳医院）；Shenzhen PKU-HKUST Medical Center（深圳北医港科大医疗中心）；Shenzhen Key Laboratory on Technology for Early Diagnosis of Major Gynecologic Diseases（深圳市重大妇科疾病早期诊断技术重点实验室）；Harvard University（哈佛大学）
A multi-centre polyp detection and segmentation dataset for generalisability assessment
University of Leeds（利兹大学）；University of Oxford（牛津大学）；Oxford National Institute for Health Research Biomedical Research Centre（牛津国家健康研究所生物医学研究中心）；SimulaMet（Simula大都会研究中心）；UiT The Arctic University of Norway（挪威北极大学）；Northwestern University（西北大学）；Arab Academy for Science and Technology（阿拉伯科学技术学院）；Veneto Institute of Oncology IOV-IRCCS（威尼托肿瘤研究所IOV-IRCCS）；CRO Centro Riferimento Oncologico IRCCS Aviano（阿维亚诺CRO肿瘤参考中心IRCCS）；University of Alexandria（亚历山大大学）；Université de Versailles St-Quentin en Yvelines（凡尔赛圣康丁昂伊夫利纳大学）；Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）
Reproducible Benchmarking for Lung Nodule Detection and Malignancy Classification Across Multiple Low-Dose CT Datasets
Duke University School of Medicine（杜克大学医学院）；Pratt School of Engineering, Duke University（杜克大学普拉特工程学院）；University of Colorado（科罗拉多大学）；Duke University（杜克大学）
Improving Clinical Target Volume Segmentation Accuracy using Anatomical Priors and Active Learning for the AGITG TOPGEAR Clinical Trial
University of New South Wales（新南威尔士大学）；Ingham Institute for Applied Medical Research（英厄姆应用医学研究所）；Liverpool and Macarthur Cancer Therapy Centres（利物浦与麦克阿瑟癌症治疗中心）；Peter MacCallum Cancer Centre（彼得·麦卡勒姆癌症中心）；University of Melbourne（墨尔本大学）；CSIRO Australian e-Health Research Centre（联邦科学与工业研究组织澳大利亚电子健康研究中心）；University of Sydney（悉尼大学）；The University of Western Australia（西澳大学）；Sir Charles Gardiner Hospital（查尔斯·加德纳爵士医院）；University of Wisconsin（威斯康星大学）；Radformation Inc.（Radformation公司）
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)
Washington University in St. Louis（圣路易斯华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）；Mallinckrodt Institute of Radiology（马林克罗特放射学研究所）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
DSU-Net: An Attention-Enhanced Dense Skip U-Net for Breast Lesion Segmentation in Mammographic Images
Department of Biomedical Engineering, University of Wisconsin-Milwaukee（威斯康星大学密尔沃基分校生物医学工程系）；Department of Mechanical Engineering, Northern Arizona University（北亚利桑那大学机械工程系）
USF-MAE: Ultrasound Self-Supervised Foundation Model with Masked Autoencoding
Department of Systems and Computer Engineering, Carleton University（系统与计算机工程系，卡尔顿大学）；Department of Clinical Science and Translational Medicine, University of Ottawa（临床科学与转化医学系，渥太华大学）；Department of Obstetrics and Gynecology, University of Ottawa（妇产科系，渥太华大学）；School of Epidemiology and Public Health, University of Ottawa（流行病学与公共卫生学院，渥太华大学）；Department of Methodological and Implementation Research, Ottawa Hospital Research Institute（方法学与实施研究系，渥太华医院研究学院）；Department of Acute Care Research, Ottawa Hospital Research Institute（急症护理研究系，渥太华医院研究学院）
RRTS Dataset: A Benchmark Colonoscopy Dataset from Resource-Limited Settings for Computer-Aided Diagnosis Research
Department of Biomedical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh（生物医学工程系，孟加拉国工程大学）；Dhaka Medical College, Dhaka, Bangladesh（达卡医学学院）
