User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation
PAII Inc.（PAII公司）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；University at Buffalo（纽约州立大学布法罗分校）；Chang Gung Memorial Hospital（长庚纪念医院）；PingAn Technology（平安科技）
Ashwin Raju, Zhanghexuan Ji, Chi Tung Cheng, Jinzheng Cai, Junzhou Huang, Jing Xiao, Le Lu, ChienHung Liao, Adam P. Harrison
Mask-based annotation of medical images, especially for 3D data, is a bottleneck in developing reliable machine learning models. Using minimal-labor user interactions (UIs) to guide the annotation is promising, but challenges remain on best harmonizing the mask prediction with the UIs. To address this, we propose the user-guided domain adaptation (UGDA) framework, which uses prediction-based adversarial domain adaptation (PADA) to model the combined distribution of UIs and mask predictions. The UIs are then used as anchors to guide and align the mask prediction. Importantly, UGDA can both learn from unlabelled data and also model the high-level semantic meaning behind different UIs. We test UGDA on annotating pathological livers using a clinically comprehensive dataset of 927 patient studies. Using only extreme-point UIs, we achieve a mean (worst-case) performance of 96.1%(94.9%), compared to 93.0% (87.0%) for deep extreme points (DEXTR). Furthermore, we also show UGDA can retain this state-of-the-art performance even when only seeing a fraction of available UIs, demonstrating an ability for robust and reliable UI-guided segmentation with extremely minimal labor demands.
AMPLIFAI: A Multiphase CT Dataset for Benchmarking Clinical Reasoning in LI-RADS Assessment of Liver Lesions
University of Maryland, College Park（马里兰大学帕克分校）；University of Maryland School of Medicine（马里兰大学医学院）；University of Maryland Institute for Health Computing（马里兰大学健康计算研究所）；University of Maryland Medical System（马里兰大学医学系统）
SRMA-Mamba: Spatial Reverse Mamba Attention Network for Pathological Liver Segmentation in MRI Volumes
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Biomedical Perception & Intelligence Lab, University of South Dakota（南达科他大学生物医学感知与智能实验室）；Aalto University（阿尔托大学）；Malaviya National Institute of Technology Jaipur（马拉维亚国家理工学院斋浦尔分校）；Machine & Hybrid Intelligence Lab, Northwestern University（西北大学机器与混合智能实验室）
TCM-Tongue: A Standardized Tongue Image Dataset with Pathological Annotations for AI-Assisted TCM Diagnosis
Beijing Technology and Business University（北京工商大学）；Xiyuan Hospital of China Academy of Chinese Medical Sciences（中国中医科学院西苑医院）；Peking University People's Hospital（北京大学人民医院）
Hunan University of Technology and Business（湖南工商大学）；Utah State University（犹他州立大学）；University of Idaho（爱达荷大学）
Liver segmentation from abdominal CT images is an essential step for liver cancer computer-aided diagnosis and surgical planning. However, both the accuracy and robustness of existing liver segmentation methods cannot meet the requirements of clinical applications. In particular, for the common clinical cases where the liver tissue contains major pathology, current segmentation methods show poor performance. In this paper, we propose a novel low-rank tensor decomposition (LRTD) based multi-atlas segmentation (MAS) framework that achieves accurate and robust pathological liver segmentation of C…
Landmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection
Center of Smart Health, School of Nursing, The Hong Kong Polytechnic University（智能健康中心、护理学院、香港理工大学）；Department of Computer Science and Engineering（计算机科学与工程系）；Institute of Medical Intelligence and XR（医学智能与XR研究所）；The Chinese University of Hong Kong（香港中文大学）；Division of Hepatobiliopancreatic Surgery, Department of General Surgery（肝胆胰外科 division, 外科部）
Stanford（斯坦福大学）；Siemens Medical Solutions USA Inc（西门子医疗解决方案美国公司）
We propose a fully-automated method for accurate and robust detection and segmentation of potentially cancerous lesions found in the liver and in lymph nodes. The process is performed in three steps, including organ detection, lesion detection and lesion segmentation. Our method applies machine learning techniques such as marginal space learning and convolutional neural networks, as well as active contour models. The method proves to be robust in its handling of extremely high lesion diversity. We tested our method on volumetric computed tomography (CT) images, including 42 volumes containing …
Domain Adaptive Multiple Instance Learning for Instance-level Prediction of Pathological Images
The University of Tokyo（东京大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Kyoto University（京都大学）；The Japanese Society of Pathology（日本病理学会）；Tokyo Medical and Dental University（东京医科齿科大学）；National Institute of Informatics（国立信息学研究所）
VEELA: A Clinically-Constrained Benchmark for Liver Vessel Segmentation in Computed Tomography Angiography
Department of Radiology, Dokuz Eylul University（多尔朱·伊勒大学放射科）；Department of Computer Engineering, Istanbul Technical University（伊斯坦布尔技术大学计算机工程系）；Institute of Natural and Applied Sciences, Dokuz Eylul University（多尔朱·伊勒大学自然科学与应用科学学院）；Department of Electrical and Electronics Engineering, Dokuz Eylul University（多尔朱·伊勒大学电气与电子工程系）；Department of Radiology, University of Wisconsin-Madison（威斯康星大学麦迪逊分校放射科）；School of Medicine, Sivas Cumhuriyet University（萨瓦斯·库尔德大学医学院）；School of Medicine, Acibadem Mehmet Ali Aydinlar University（阿克塞姆·梅赫梅特·阿里·阿迪姆大学医学院）；Department of Artificial Intelligence Engineering, Bahçeşehir University（巴切希尔大学人工智能工程系）；Faculty of Pharmacy, Sivas Cumhuriyet University（萨瓦斯·库尔德大学药学院）
Zhejiang University（浙江大学）；Stevens Institute of Technology（斯蒂文斯理工学院）；Zhejiang Lab（之江实验室）；Zhejiang University of Technology（浙江工业大学）；Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究中心）
The microvascular invasion (MVI) is a major prognostic factor in hepatocellular carcinoma, which is one of the malignant tumors with the highest mortality rate. The diagnosis of MVI needs discovering the vessels that contain hepatocellular carcinoma cells and counting their number in each vessel, which depends heavily on experiences of the doctor, is largely subjective and time-consuming. However, there is no algorithm as yet tailored for the MVI detection from pathological images. This paper collects the first pathological liver image dataset containing 522 whole slide images with labels of v…
University of Arizona（亚利桑那大学）；Houston Methodist Hospital（休斯顿卫理公会医院）
Obtaining manual annotations for large datasets for supervised training of deep learning (DL) models is challenging. The availability of large unlabeled datasets compared to labeled ones motivate the use of self-supervised pretraining to initialize DL models for subsequent segmentation tasks. In this work, we consider two pre-training approaches for driving a DL model to learn different representations using: a) regression loss that exploits spatial dependencies within an image and b) contrastive loss that exploits semantic similarity between pairs of images. The effect of pretraining techniqu…
PAII Inc.（PAII公司）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Chang Gung Memorial Hospital（长庚纪念医院）；PingAn Technology（平安科技）
In medical imaging, organ/pathology segmentation models trained on current publicly available and fully-annotated datasets usually do not well-represent the heterogeneous modalities, phases, pathologies, and clinical scenarios encountered in real environments. On the other hand, there are tremendous amounts of unlabelled patient imaging scans stored by many modern clinical centers. In this work, we present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe), which only requires a small labeled cohort of single phase imaging data to adapt to any unlabeled cohort of …
Yale University（耶鲁大学）；Yale School of Medicine（耶鲁大学医学院）；University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；Chinese Academy of Sciences（中国科学院）
Multimodal image registration has many applications in diagnostic medical imaging and image-guided interventions, such as Transcatheter Arterial Chemoembolization (TACE) of liver cancer guided by intraprocedural CBCT and pre-operative MR. The ability to register peri-procedurally acquired diagnostic images into the intraprocedural environment can potentially improve the intra-procedural tumor targeting, which will significantly improve therapeutic outcomes. However, the intra-procedural CBCT often suffers from suboptimal image quality due to lack of signal calibration for Hounsfield unit, limi…
University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Lausanne University Hospital（洛桑大学医院）；University of Lausanne（洛桑大学）；Technical University of Munich（慕尼黑工业大学）；Brain Research Institute, University of Zurich（苏黎世大学脑研究所）；University of Debrecen（德布勒森大学）
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders. To facilitate this analysis, automatic multi-tissue fetal brain segmentation algorithms are needed, which in turn requires open databases of segmented fetal brains. Here we introduce a publicly available database of 50 manually segmented pathological and non-pathological fetal magnetic resonance brain volume reconstructions across a range of gestational ages (20 to 33 weeks) into 7 different tissue categories (extern…
How Far Has AI Come in Liver Fibrosis Staging? A Large-Scale Real-World Dataset and Benchmark
School of Data Science, Fudan University, Shanghai, China（复旦大学数据科学学院）；Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China（复旦大学上海公共卫生临床中心放射科）；Department of Electrical and Computer Engineering, Northwestern University, Evanston, USA（西北大学电气与计算机工程系）；Shanghai Key Laboratory of Flexible Medical Robotics, Tongren Hospital, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China（上海柔性医疗机器人重点实验室）；School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China（上海交通大学生物医学工程学院）；School of Computer Science, University of Nottingham, Nottingham, UK（诺丁汉大学计算机科学学院）；Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China（上海交通大学生物医学工程学院医疗机器人研究所）；College of Computer Science and Technology, Huaqiao University, Xiamen, China（华侨大学计算机科学与技术学院）；School of Electronic Information (School of Artificial Intelligence), Northwest University, Xi'an, China（西北大学电子信息学院（人工智能学院））；Department of Mechanical Engineering, University College London, London, UK（伦敦大学学院机械工程系）；Institute of Neuroscience and Cardiovascular Research, University of Edinburgh, Edinburgh, UK（爱丁堡大学神经科学与心血管研究学院）；CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology, Beijing, China（中国科学院纳米科学卓越中心）；School of Control Science and Engineering, Shandong University, Jinan, China（山东大学控制科学与工程学院）；School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, UK（伯明翰大学工程学院）
A Hyperspectral Imaging Dataset and Methodology for Intraoperative Pixel-Wise Classification of Metastatic Colon Cancer in the Liver
Ruđer Bošković Institute（鲁杰尔·博什科维奇研究所）；Technical University Munich（慕尼黑工业大学）；University of Montreal（蒙特利尔大学）；Photon etc.（Photon etc.公司）；Clinical Hospital Center Zagreb（萨格勒布临床医院中心）；University of Zagreb（萨格勒布大学）；Clinical Hospital Dubrava（杜布拉瓦临床医院）
TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港理工大学电子与计算机工程系）；AI Center of Excellence, Ain Shams University（爱思明大学人工智能中心）；Department of Radiology, Ain Shams University（爱思明大学放射科）；Department of Radiology, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University（广东省恶性肿瘤表观遗传与基因调控重点实验室，中山大学孙逸仙纪念医院放射科）；Nanfang Hospital, Southern Medical University（南方医科大学南华医院）；Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany（德国癌症研究中心（DKFZ）医学影像计算部，海德堡，德国）；Medical Faculty Heidelberg, Heidelberg University（海德堡大学医学院）；Faculty of Mathematics and Computer Science, Heidelberg University（海德堡大学数学与计算机科学学院）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院肿瘤医院（国家癌症中心/国家临床肿瘤医学中心）深圳医院）；Research Institute of Tsinghua, Guangzhou（清华大学广州研究院）
PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation
Baidu Inc.（百度公司）；Sun Yat-sen University（中山大学）；Agency for Science, Technology and Research（新加坡科技研究局）；CONICET（阿根廷国家科学与技术研究理事会）；UNICEN（阿根廷国立中央大学）；Medical University of Vienna（维也纳医科大学）
Ecole Polytechnique Montréal（蒙特利尔理工学院）；CHU Sainte-Justine Hospital Research Center（圣贾斯汀儿童医院研究中心）；University of Montreal（蒙特利尔大学）
The early detection, diagnosis and monitoring of liver cancer progression can be achieved with the precise delineation of metastatic tumours. However, accurate automated segmentation remains challenging due to the presence of noise, inhomogeneity and the high appearance variability of malignant tissue. In this paper, we propose an unsupervised metastatic liver tumour segmentation framework using a machine learning approach based on discriminant Grassmannian manifolds which learns the appearance of tumours with respect to normal tissue. First, the framework learns within-class and between-class…
MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset
Shanghai AI Lab（上海人工智能实验室）；University of Liverpool（利物浦大学）；Columbia university（哥伦比亚大学）；Monash University（墨尔本大学）；Xi'an Jiaotong-Liverpool University（西安交通大学利物浦大学）；MBZUAI（穆斯林人工智能研究所）；People's Hospital of Gong'an County（公安县人民医院）
本文提出首个 3D 全腹部病变多类分割数据集 MSWAL，覆盖七类常见病变，并提出 Inception nnU-Net 框架，在分割性能上显著优于现有方法。
Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine（浙江大学医学院附属杭州市第一人民医院）；Syracuse University（雪城大学）；Zhejiang University（浙江大学）；The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院）
This study aimed to develop a classifier of prognosis after resection or liver transplantation (LT) for HCC by directly analysing the ubiquitously available histological images using deep learning based neural networks. Nucleus map set was used to train U-net to capture the nuclear architectural information. Train set included the patients with HCC treated by resection and has a distinct outcome. LT set contained patients with HCC treated by LT. Train set and its nuclear architectural information extracted by U-net were used to train MobileNet V2 based classifier (MobileNetV2_HCC_Class), purpo…
Histopathology images are gigapixel-sized and include features and information at different resolutions. Collecting annotations in histopathology requires highly specialized pathologists, making it expensive and time-consuming. Self-training can alleviate annotation constraints by learning from both labeled and unlabeled data, reducing the amount of annotations required from pathologists. We study the design of teacher-student self-training systems for Non-alcoholic Steatohepatitis (NASH) using clinical histopathology datasets with limited annotations. We evaluate the models on in-distribution…
Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment. However, it is still a very challenging task due to the complex background, fuzzy boundary, and various appearance of liver. In this paper, we propose an automatic and efficient algorithm to segment liver from 3D CT volumes. A deep image-to-image network (DI2IN) is first deployed to generate the liver segmentation, employing a convolutional encoder-decoder architecture combined with multi-level feature…
Massachusetts Institute of Technology（麻省理工学院）；Harvard University（哈佛大学）；Howard Hughes Medical Institute（霍华德·休斯医学研究所）
Global eradication of malaria depends on the development of drugs effective against the silent, yet obligate liver stage of the disease. The gold standard in drug development remains microscopic imaging of liver stage parasites in in vitro cell culture models. Image analysis presents a major bottleneck in this pipeline since the parasite has significant variability in size, shape, and density in these models. As with other highly variable datasets, traditional segmentation models have poor generalizability as they rely on hand-crafted features; thus, manual annotation of liver stage malaria im…
AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons
BAMF Health（BAMF健康公司）；National Institute of Health（美国国立卫生研究院）；Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究实验室）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）
BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset
Australia Institute of Machine Learning, University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Australian National University（澳大利亚国立大学）；Flinders Health and Medical Research Institute, Flinders University（弗林德斯大学弗林德斯健康与医学研究所）；ETH Zürich（苏黎世联邦理工学院）
Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images
Division of Computing and Data Science, Ruđer Bošković Institute（鲁德·波斯科维奇研究所计算与数据科学部）；Sitnik Ventures UG（Sitnik Ventures公司）；Department of Pathology and Cytology, Dubrava University Hospital（杜布拉瓦大学医院病理科与细胞学部）；Clinical Division for Pathology and Cytology, Clinical Hospital Center Zagreb（扎格雷布临床医院中心病理科与细胞学部）；Division of Molecular Medicine, Ruđer Bošković Institute（鲁德·波斯科维奇研究所分子医学部）
Enhancing efficiency in paediatric brain tumour segmentation using a pathologically diverse single-center clinical dataset
University Children's Hospital Zurich（苏黎世大学儿童医院）；Technical University of Munich（慕尼黑工业大学）；Helmholtz Zentrum（亥姆霍兹中心）；German Consortium for Translational Cancer Research (DKTK)（德国转化癌症研究联盟）；Georgia Institute of Technology（佐治亚理工学院）；Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）；Helmholtz AI（亥姆霍兹人工智能）；Helmholtz Zentrum Munich（慕尼黑亥姆霍兹中心）；University of Zurich（苏黎世大学）
Department of Computer Science, Vanderbilt University（范德堡大学计算机科学系）
Histopathology has played an essential role in cancer diagnosis. With the rapid advances in convolutional neural networks (CNN). Various CNN-based automated pathological image segmentation approaches have been developed in computer-assisted pathological image analysis. In the past few years, Transformer neural networks (Transformer) have shown the unique merit of capturing the global long-distance dependencies across the entire image as a new deep learning paradigm. Such merit is appealing for exploring spatially heterogeneous pathological images. However, there have been very few, if any, stu…
National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Technical University of Munich（慕尼黑工业大学）；Zhejiang Institute of Standardization（浙江省标准化研究院）；National Clinical Research Centre of Kidney Diseases, Nanjing University（南京大学国家肾脏疾病临床医学研究中心）
Tissue segmentation is the mainstay of pathological examination, whereas the manual delineation is unduly burdensome. To assist this time-consuming and subjective manual step, researchers have devised methods to automatically segment structures in pathological images. Recently, automated machine and deep learning based methods dominate tissue segmentation research studies. However, most machine and deep learning based approaches are supervised and developed using a large number of training samples, in which the pixelwise annotations are expensive and sometimes can be impossible to obtain. This…
Automatic liver lesion segmentation is a challenging task while having a significant impact on assisting medical professionals in the designing of effective treatment and planning proper care. In this paper we propose a cascaded system that combines both 2D and 3D convolutional neural networks to effectively segment hepatic lesions. Our 2D network operates on a slice by slice basis to segment the liver and larger tumors, while we use a 3D network to detect small lesions that are often missed in a 2D segmentation design. We employ this algorithm on the LiTS challenge obtaining a Dice score per …
Shenzhen Research Institute of Big Data, the Chinese University of Hong Kong（香港中文大学深圳大数据研究院）；Huazhong University of Science and Technology（华中科技大学）；Shenzhen Third People’s Hospital（深圳市第三人民医院）
With the development of radiomics, noninvasive diagnosis like ultrasound (US) imaging plays a very important role in automatic liver fibrosis diagnosis (ALFD). Due to the noisy data, expensive annotations of US images, the application of Artificial Intelligence (AI) assisting approaches encounters a bottleneck. Besides, the use of mono-modal US data limits the further improve of the classification results. In this work, we innovatively propose a multi-modal fusion network with active learning (MMFN-AL) for ALFD to exploit the information of multiple modalities, eliminate the noisy data and red…
Primary tumors have a high likelihood of developing metastases in the liver and early detection of these metastases is crucial for patient outcome. We propose a method based on convolutional neural networks (CNN) to detect liver metastases. First, the liver was automatically segmented using the six phases of abdominal dynamic contrast enhanced (DCE) MR images. Next, DCE-MR and diffusion weighted (DW) MR images are used for metastases detection within the liver mask. The liver segmentations have a median Dice similarity coefficient of 0.95 compared with manual annotations. The metastases detect…
Huazhong University of Science and Technology（华中科技大学）；The Chinese University of Hong Kong – Shenzhen（香港中文大学（深圳））；Johns Hopkins University（约翰斯·霍普金斯大学）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）
We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors; (II) effective for training AI models, which can perform liver tumor segmentation similarly to the model trained on real tumors -- this result is exciting because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to real tumors. This result also impl…
Accurate visualization of liver tumors and their surrounding blood vessels is essential for noninvasive diagnosis and prognosis prediction of tumors. In medical image segmentation, there is still a lack of in-depth research on the simultaneous segmentation of liver tumors and peritumoral blood vessels. To this end, we collect the first liver tumor, and vessel segmentation benchmark datasets containing 52 portal vein phase computed tomography images with liver, liver tumor, and vessel annotations. In this case, we propose a 3D U-shaped Cross-Attention Network (UCA-Net) that utilizes a tailored …
VLM-CPL: Consensus Pseudo Labels from Vision-Language Models for Annotation-Free Pathological Image Classification
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China（电子科技大学机械与电子工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Department of Pathology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China（pathology department, 四川省癌症临床研究中心, 四川省肿瘤医院及研究所, 电子科技大学附属肿瘤医院）；Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, University of Electronic Science and Technology of China（放射肿瘤科, 四川省肿瘤医院及研究所, 电子科技大学）
A Hierarchical Geometry-guided Transformer for Histological Subtyping of Primary Liver Cancer
Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, China（病理科、南京鼓楼医院、南京大学附属医院）；Department of Biostatistics, School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109-2029（生物统计学系、公共卫生学院）
Xi’an Jiaotong University（西安交通大学）；Pennsylvania State University（宾夕法尼亚州立大学）；the First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院）
Digital pathology plays a crucial role in the development of artificial intelligence in the medical field. The digital pathology platform can make the pathological resources digital and networked, and realize the permanent storage of visual data and the synchronous browsing processing without the limitation of time and space. It has been widely used in various fields of pathology. However, there is still a lack of an open and universal digital pathology platform to assist doctors in the management and analysis of digital pathological sections, as well as the management and structured descripti…
Central South University（中南大学）；Shaanxi Provincial People’s Hospital（陕西省人民医院）；Xiangya Hospital, Central South University（中南大学湘雅医院）
[Purpose] The pathology is decisive for disease diagnosis, but relies heavily on the experienced pathologists. Recently, pathological artificial intelligence (PAI) is thought to improve diagnostic accuracy and efficiency. However, the high performance of PAI based on deep learning in the laboratory generally cannot be reproduced in the clinic. [Methods] Because the data preparation is important for PAI, the paper has reviewed PAI-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. The in-depth analysis of methods for preparing dat…
Artificial Intelligence-Enhanced Couinaud Segmentation for Precision Liver Cancer Therapy
Stanford University（斯坦福大学）；University of Southern California（南加州大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Wake Forest University（维克森林大学）；Shandong Provincial Hospital Affiliated to Shandong First Medical University（山东第一医科大学附属山东省立医院）；Shandong First Medical University（山东第一医科大学）
Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI
Ho Chi Minh University of Science（胡志明科技大学）；National Central University（国立中央大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Houston（休斯顿大学）；Northwestern University（西北大学）
ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
Multi-modal Liver Segmentation and Fibrosis Staging Using Real-world MRI Images
Multiscale X-ray Imaging (MXI) Lab, Department of Mechanical Engineering, University College London（多尺度X射线成像实验室，机械工程系，伦敦大学学院）；Centre for Clinical Brain Sciences, University of Edinburgh（临床脑科学中心，爱丁堡大学）；MRC Weatherall Institute of Molecular Medicine, University of Oxford（MRC韦伯尔分子医学研究所，牛津大学）
Mask the Unknown: Assessing Different Strategies to Handle Weak Annotations in the MICCAI2023 Mediastinal Lymph Node Quantification Challenge
School of Computation, Information and Technology, Technical University Munich（慕尼黑工业大学计算、信息与技术学院）；Department of RadioOncology, Klinikum rechts der Isar, Technical University Munich（慕尼黑工业大学右岸医院放射肿瘤科）；Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich（亥姆霍兹慕尼黑生物医学影像机器学习研究所）；Munich Center of Machine Learning (MCML)（慕尼黑机器学习中心）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与影像科学学院）
Uncertainty-Aware Segmentation Quality Prediction via Deep Learning Bayesian Modeling: Comprehensive Evaluation and Interpretation on Skin Cancer and Liver Segmentation
BCN Medtech（BCN医学科技）；Scientific and Technical Department, Sycai Technologies S.L.（Sycai技术部门）
