Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Whole-slide imaging which is a method of scanning glass slides have been employed for diagnosis of HCC. Using high resolution Whole-slide images is infeasible for Convolutional Neural Network applications. Hence tiling the Whole-slide images is a common methodology for assigning Convolutional Neural Networks for classification and segmentation. Determination of the tile size affects the performance of the algorithms since small field of view can not capture the information on a larger scale and large field of view can not capture the information on a cellular scale. In this work, the effect of tile size on performance for classification problem is analysed. In addition, Multi Field of View CNN is assigned for taking advantage of the information provided by different tile sizes and Attention CNN is assigned for giving the capability of voting most contributing tile size. It is found that employing more than one tile size significantly increases the performance of the classification by 3.97% and both algorithms are found successful over the algorithm which uses only one tile size.
Technische Universität Berlin（柏林工业大学）；Aignostics GmbH（Aignostics 公司）；Charité Universitätsmedizin Berlin（柏林夏里特医学院）；Berlin Institute of Health at Charité – Universitätsmedizin Berlin（柏林健康研究所（夏里特医学院））；University Hospital Düsseldorf（杜塞尔多夫大学医院）；Ludwig-Maximilians-Universität München（慕尼黑大学）；German Cancer Research Center（德国癌症研究中心）
In this paper, we present a deep learning segmentation approach to classify and quantify the two most prevalent primary liver cancers - hepatocellular carcinoma and intrahepatic cholangiocarcinoma - from hematoxylin and eosin (H&E) stained whole slide images. While semantic segmentation of medical images typically requires costly pixel-level annotations by domain experts, there often exists additional information which is routinely obtained in clinical diagnostics but rarely utilized for model training. We propose to leverage such weak information from patient diagnoses by deriving complementa…
Yale University（耶鲁大学）；University of Washington（华盛顿大学）
This work proposes a pipeline to predict treatment response to intra-arterial therapy of patients with Hepatocellular Carcinoma (HCC) for improved therapeutic decision-making. Our graph neural network model seamlessly combines heterogeneous inputs of baseline MR scans, pre-treatment clinical information, and planned treatment characteristics and has been validated on patients with HCC treated by transarterial chemoembolization (TACE). It achieves Accuracy of $0.713 \pm 0.075$, F1 of $0.702 \pm 0.082$ and AUC of $0.710 \pm 0.108$. In addition, the pipeline incorporates uncertainty estimation to…
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The First Affiliated Hospital of Jinan University（暨南大学附属第一医院）
Automatic segmentation of hepatocellular carcinoma (HCC) in Digital Subtraction Angiography (DSA) videos can assist radiologists in efficient diagnosis of HCC and accurate evaluation of tumors in clinical practice. Few studies have investigated HCC segmentation from DSA videos. It shows great challenging due to motion artifacts in filming, ambiguous boundaries of tumor regions and high similarity in imaging to other anatomical tissues. In this paper, we raise the problem of HCC segmentation in DSA videos, and build our own DSA dataset. We also propose a novel segmentation network called DSA-LT…
Mask-guided Data Augmentation for Multiparametric MRI Generation with a Rare Hepatocellular Carcinoma
Universidad Industrial de Santander（桑坦德工业大学）；University of Toulouse（图卢兹大学）；Université Paul Sabatier Toulouse 3（图卢兹第三大学（保罗·萨巴蒂埃大学））；Bicêtre University Hospital（比塞特尔大学医院）；Assistance publique des hôpitaux de Paris（巴黎公共援助医院）；Université Claude Bernard Lyon 1（里昂第一大学（克洛德·贝尔纳里昂第一大学））
A Hierarchical Transformer Encoder to Improve Entire Neoplasm Segmentation on Whole Slide Image of Hepatocellular Carcinoma
Université Paris Cité（巴黎西岱大学）；University of Applied Sciences of Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学（瓦莱州高等专业学院））；French University Institute (IUF)（法国大学研究院）
针对肝细胞癌 WSI 全肿瘤分割难题，提出分层 Transformer 架构 HiTrans，在 4096×4096 图像块上学习全局依赖并提升分割性能。
Chang Gung Memorial Hospital（长庚纪念医院）；Chang Gung University（长庚大学）；PAII Inc.（PAII公司）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）
Hepatocellular carcinoma (HCC) can be potentially discovered from abdominal computed tomography (CT) studies under varied clinical scenarios, e.g., fully dynamic contrast enhanced (DCE) studies, non-contrast (NC) plus venous phase (VP) abdominal studies, or NC-only studies. We develop a flexible three-dimensional deep algorithm, called hetero-phase volumetric detection (HPVD), that can accept any combination of contrast-phase inputs and with adjustable sensitivity depending on the clinical purpose. We trained HPVD on 771 DCE CT scans to detect HCCs and tested on external 164 positives and 206 …
A baseline for machine-learning-based hepatocellular carcinoma diagnosis using multi-modal clinical data
Heidelberg University（海德堡大学）；Mannheim Institute for Intelligent Systems in Medicine（曼海姆医学智能系统研究所）；UMM Mannheim（曼海姆大学医学中心）；Mannheim Medical Faculty（曼海姆医学院）；Clinic for Radiology and Nuclear Medicine（放射科与核医学科）；Interdisciplinary Center for Scientific Computing（跨学科科学计算中心）；Central Institute for Computer Engineering（中央计算机工程研究所）；Heidelberg Center for Model-Based AI, Data Analysis and Modeling in Medicine（海德堡基于模型的人工智能、医学数据分析与建模中心）；School of Information Engineering, Nanjing University of Finance and Economics（南京财经大学信息工程学院）
BioFact-MoE: Biologically Factorized Mixture of Experts for Vision-Language Prognostic Modeling in Hepatocellular Carcinoma
Department of Radiology \& Biomedical Imaging, Department of Biomedical Engineering, Department of Electrical Engineering, Department of Statistics \& Data Science Yale University, New Haven, CT, 06510, USA
Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局资讯通信研究院）；National University of Singapore（新加坡国立大学）；Singapore General Hospital（新加坡中央医院）；National Cancer Centre（新加坡国立癌症中心）
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the fourth most common cause of cancer-related death worldwide. Understanding the underlying gene mutations in HCC provides great prognostic value for treatment planning and targeted therapy. Radiogenomics has revealed an association between non-invasive imaging features and molecular genomics. However, imaging feature identification is laborious and error-prone. In this paper, we propose an end-to-end deep learning framework for mutation prediction in APOB, COL11A1 and ATRX genes using multiphasic CT scans. Con…
Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images
Visvesvaraya National Institute of Technology（维斯瓦拉亚国家理工学院）；AIIMS Nagpur（那格浦尔全印度医学科学研究所）；Indian Institute of Technology Mandi（曼迪印度理工学院）；Universitat de Barcelona（巴塞罗那大学）
Computer-aided diagnosis (CAD) can be used as an important tool to aid and enhance pathologists' diagnostic decision-making. Deep learning techniques, such as convolutional neural networks (CNN) and fully convolutional networks (FCN), have been successfully applied in medical and biological research. Unfortunately, histological image segmentation is often constrained by the availability of labeled training data once labeling histological images for segmentation purposes is a highly-skilled, complex, and time-consuming task. This paper presents the hematoxylin and eosin (H&E) stained oral cavit…
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…
A Retrospective Systematic Study on Hierarchical Sparse Query Transformer-assisted Ultrasound Screening for Early Hepatocellular Carcinoma
Northwestern Polytechnical University（西北工业大学）；The First Affiliated Hospital of Sun Yat-Sen University（中山大学附属第一医院）；The Seventh Affiliated Hospital of Sun Yat-Sen University（中山大学附属第七医院）；The First Affiliated Hospital of Guangxi Medical University（广西医科大学附属第一医院）；Xi’an Jiaotong University（西安交通大学）
A Polarization and Radiomics Feature Fusion Network for the Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma
University College London（伦敦大学学院）；Tsinghua University（清华大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Tsinghua-Berkeley Shenzhen Institute（清华-伯克利深圳学院）；Fujian Medical University Cancer Hospital（福建医科大学肿瘤医院）；Fujian Cancer Hospital（福建省肿瘤医院）
Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma by Integrating Multimodal Ultrasound and Clinical Data: A Multicenter Study
Shenzhen University（深圳大学）；Peking University Shenzhen Hospital（北京大学深圳医院）；Chinese PLA General Hospital（中国人民解放军总医院）
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…
Hepato-LLaVA: An Expert MLLM with Sparse Topo-Pack Attention for Hepatocellular Pathology Analysis on Whole Slide Images
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学）；Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（pathology department, 国家癌症中心/国家癌症临床研究中心/癌症医院, 中国医学科学院和北京协和医学院）
Breast Cancer is a major cause of death worldwide among women. Hematoxylin and Eosin (H&E) stained breast tissue samples from biopsies are observed under microscopes for the primary diagnosis of breast cancer. In this paper, we propose a deep learning-based method for classification of H&E stained breast tissue images released for BACH challenge 2018 by fine-tuning Inception-v3 convolutional neural network (CNN) proposed by Szegedy et al. These images are to be classified into four classes namely, i) normal tissue, ii) benign tumor, iii) in-situ carcinoma and iv) invasive carcinoma. Our strate…
Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area. In this paper, we present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). We first train a CNN using high pixel resolution patches to capture cellular level information. The feature responses generated by this model are then fed as input to a second CNN, stacked on top of the first. Training of this stac…
Northwestern Polytechnical University（西北工业大学）；Tianjin Medical University（天津医科大学）
Goal: Squamous cell carcinoma of cervix is one of the most prevalent cancer worldwide in females. Traditionally, the most indispensable diagnosis of cervix squamous carcinoma is histopathological assessment which is achieved under microscope by pathologist. However, human evaluation of pathology slide is highly depending on the experience of pathologist, thus big inter- and intra-observer variability exists. Digital pathology, in combination with deep learning provides an opportunity to improve the objectivity and efficiency of histopathologic slide analysis. Methods: In this study, we obtaine…
Clear cell renal cell carcinoma (ccRCC) is one of the most common forms of intratumoral heterogeneity in the study of renal cancer. ccRCC originates from the epithelial lining of proximal convoluted renal tubules. These cells undergo abnormal mutations in the presence of Ki67 protein and create a lump-like structure through cell proliferation. Manual counting of tumor cells in the tissue-affected sections is one of the strongest prognostic markers for renal cancer. However, this procedure is time-consuming and also prone to subjectivity. These assessments are based on the physical cell appeara…
Lung squamous cell carcinoma (LSCC) has a high recurrence and metastasis rate. Factors influencing recurrence and metastasis are currently unknown and there are no distinct histopathological or morphological features indicating the risks of recurrence and metastasis in LSCC. Our study focuses on the recurrence prediction of LSCC based on H&E-stained histopathological whole-slide images (WSI). Due to the small size of LSCC cohorts in terms of patients with available recurrence information, standard end-to-end learning with various convolutional neural networks for this task tends to overfit. Al…
This paper presents results of applying Inception v4 deep convolutional neural network to ICIAR-2018 Breast Cancer Classification Grand Challenge, part a. The Challenge task is to classify breast cancer biopsy results, presented in form of hematoxylin and eosin stained images. Breast cancer classification is of primary interest to the medical practitioners and thus binary classification of breast cancer images have been under investigation by many researchers, but multi-class categorization of histology breast images have been challenging due to the subtle differences among the categories. In …
Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade. Deep neural networks achieve outstanding accuracy in medical patterns recognition, however they require large sets of annotated training images. We introduce UniToPatho, an annotated dataset of 9536 hematoxylin and eosin (H&E) stained patches extracted from 292 whole-…
Recent breakthroughs in object detection and image classification using Convolutional Neural Networks (CNNs) are revolutionizing the state of the art in medical imaging, and microscopy in particular presents abundant opportunities for computer vision algorithms to assist medical professionals in diagnosis of diseases ranging from malaria to cancer. High resolution scans of microscopy slides called Whole Slide Images (WSIs) offer enough information for a cancer pathologist to come to a conclusion regarding cancer presence, subtype, and severity based on measurements of features within the slide…
National Cancer Institute – IRCCS – Fondazione Pascale（国家癌症研究所——IRCCS——帕斯卡尔基金会）；IBM Research – Zurich（IBM研究院——苏黎世）；ETH Zurich（苏黎世联邦理工学院）；Institute for High Performance Computing and Networking of the Research Council of Italy, ICAR-CNR（意大利研究理事会高性能计算与网络研究所，ICAR-CNR）；University of Naples Federico II（那不勒斯费德里科二世大学）；EPFL Lausanne（洛桑联邦理工学院）
Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women with cancer. Recent advancements in diagnostic activities combined with large-scale screening policies have significantly lowered the mortality rates for breast cancer patients. However, the manual inspection of tissue slides by the pathologists is cumbersome, time-consuming, and is subject to significant inter- and intra-observer variability. Recently, the advent of whole-slide scanning systems have empowered the rapid digitization of pathology slides, and enabled to develop digital workfl…
OMG-Net: A Deep Learning Framework Deploying Segment Anything to Detect Pan-Cancer Mitotic Figures from Haematoxylin and Eosin-Stained Slides
University College London（伦敦大学学院）；University College London Hospitals NHS Foundation Trust（伦敦大学学院医院 NHS 基金会信托）；University College London Cancer Institute（伦敦大学学院癌症研究所）；Royal National Orthopaedic Hospital NHS Foundation Trust（皇家国立骨科医院 NHS 基金会信托）；University Hospital Basel（巴塞尔大学医院）；University of Basel（巴塞尔大学）；Rutgers Biomedical and Health Sciences（罗格斯生物医学与健康科学学院）；Rutgers University（罗格斯大学）；The Robert Jones and Agnes Hunt Orthopaedic Hospital（罗伯特·琼斯和艾格尼丝·亨特骨科医院）；Keele University（基尔大学）
Neuromation；Massachusetts Institute of Technology（麻省理工学院）；Lyft Inc.（Lyft公司）；University of Michigan（密歇根大学）
Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge…
The Pennsylvania State University（宾夕法尼亚州立大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Upper Dublin High School（上达比高中）
Generative models have been applied in the medical imaging domain for various image recognition and synthesis tasks. However, a more controllable and interpretable image synthesis model is still lacking yet necessary for important applications such as assisting in medical training. In this work, we leverage the efficient self-attention and contrastive learning modules and build upon state-of-the-art generative adversarial networks (GANs) to achieve an attribute-aware image synthesis model, termed AttributeGAN, which can generate high-quality histopathology images based on multi-attribute input…
Sun Yat-sen University（中山大学）；The First Affiliated Hospital, Sun Yat-sen University（中山大学附属第一医院）；Infervision Inc.（推想医疗科技股份有限公司）
Hepatocellular carcinoma (HCC) is the second most frequent cause of malignancy-related death and is one of the diseases with the highest incidence in the world. Because the liver is the only organ in the human body that is supplied by two major vessels: the hepatic artery and the portal vein, various types of malignant tumors can spread from other organs to the liver. And due to the liver masses' heterogeneous and diffusive shape, the tumor lesions are very difficult to be recognized, thus automatic lesion detection is necessary for the doctors with huge workloads. To assist doctors, this work…
Vision Transformer-Based Deep Learning for Histologic Classification of Endometrial Cancer
Dartmouth College（达特茅斯学院）；Dartmouth-Health（达特茅斯健康中心）；Geisel School of Medicine, Dartmouth College（达特茅斯学院盖泽尔医学院）；Leiden University Medical Center（莱顿大学医学中心）
Technische Universit\"at M\"unchen, Image-Based Biomedical Modeling Group, Arccisstrasse 21, 80333 Munich Technische Universit\"at M\"unchen, Institute for diagnostic；interventional Radiology, Ismaninger Str. 22, 81675 Munich + Ludwig-Maximilians-Universit\"at, German Center for Vertigo
Automatic non-invasive assessment of hepatocellular carcinoma (HCC) malignancy has the potential to substantially enhance tumor treatment strategies for HCC patients. In this work we present a novel framework to automatically characterize the malignancy of HCC lesions from DWI images. We predict HCC malignancy in two steps: As a first step we automatically segment HCC tumor lesions using cascaded fully convolutional neural networks (CFCN). A 3D neural network (SurvivalNet) then predicts the HCC lesions' malignancy from the HCC tumor segmentation. We formulate this task as a classification prob…
ETH Zurich（苏黎世联邦理工学院）；EPFL（洛桑联邦理工学院）；IBM Research（IBM研究院）；IRCCS-Fondazione Pascale（IRCCS-帕斯卡尔基金会）；ICAR-CNR（意大利国家研究理事会ICAR研究所）
Cancer diagnosis, prognosis, and therapeutic response prediction are heavily influenced by the relationship between the histopathological structures and the function of the tissue. Recent approaches acknowledging the structure-function relationship, have linked the structural and spatial patterns of cell organization in tissue via cell-graphs to tumor grades. Though cell organization is imperative, it is insufficient to entirely represent the histopathological structure. We propose a novel hierarchical cell-to-tissue-graph (HACT) representation to improve the structural depiction of the tissue…
Heterogeneous Image-based Classification Using Distributional Data Analysis
MD Anderson Cancer Center（MD安德森癌症中心）；UT Southwestern Medical Center（西南医学中心）；UC Davis Comprehensive Cancer Center（加州大学戴维斯分校综合癌症中心）；Baylor College of Medicine（贝勒医学院）
REACT-KD: Region-Aware Cross-modal Topological Knowledge Distillation for Interpretable Medical Image Classification
Department of Health Technology and Informatics, Hong Kong Polytechnic University（健康科技与信息技术系，香港理工大学）；Department of Nuclear Medicine and PET, Hong Kong Sanatorium and Hospital（核医学与PET部门，香港疗养院及医院）
Accurate and Scalable Multimodal Pathology Retrieval via Attentive Vision-Language Alignment
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科学与技术大学计算机科学与工程系）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属同济医院放射科）；Department of Pathology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University（浙江大学医学院附属邵氏医院病理科）；Department of Pathology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology（华中科技大学同济医学院附属武汉中心医院病理科）；Department of Pathology, The First Affiliated Hospital, School of Medicine, Zhejiang University（浙江大学医学院附属第一医院病理科）；Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology（香港科学与技术大学化学与生物工程系）；Division of Life Science, The Hong Kong University of Science and Technology（香港科学与技术大学生命科学系）
University of Warwick（华威大学）；University of Nottingham（诺丁汉大学）；Indian Institute of Technology Guwahati（印度理工学院古瓦哈提分校）；Maynooth University（梅努斯大学）；NLP Logix LLC（NLP Logix有限责任公司）；University of Castilla-La Mancha（卡斯蒂利亚-拉曼恰大学）；University of Canterbury（坎特伯雷大学）；Friedrich Schiller University of Jena（耶拿弗里德里希·席勒大学）；MSD International GmbH（MSD国际有限公司）；Singapore Agency for Science, Technology and Research（新加坡科技研究局）；University Hospitals Coventry and Warwickshire（考文垂和沃里克郡大学医院）
Evaluating expression of the Human epidermal growth factor receptor 2 (Her2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognised importance as a predictive and prognostic marker in clinical practice. However, visual scoring of Her2 is subjective and consequently prone to inter-observer variability. Given the prognostic and therapeutic implications of Her2 scoring, a more objective method is required. In this paper, we report on a recent automated Her2 scoring contest, held in conjunction wit…
