目的——刻画并评估已发表的、利用组织病理学数据评估人工智能（AI）方法用于卵巢癌诊断或预后的研究质量。方法——截至2023年5月19日，对PubMed、Scopus、Web of Science、CENTRAL和WHO-ICTRP数据库进行检索。纳入标准要求研究需评估基于组织病理学图像的AI在卵巢癌诊断或预后推断中的应用。采用PROBAST（预测模型偏倚风险评估工具）评估偏倚风险，将每个目标模型的相关信息制成表格并报告汇总统计量，遵循PRISMA 2020报告规范。结果——共检索到1573条记录，其中45项符合纳入标准。纳入研究包含80个目标模型，其中37个为诊断模型，22个为预后模型，21个为具有其他诊断相关结局的模型。模型构建使用了来自1-776名卵巢癌患者的1-1375张切片。模型结局包括治疗应答（11/80）、恶性状态（10/80）、染色量（9/80）和组织学亚型（7/80）。所有模型整体偏倚风险均为高或不明确，多数研究在分析环节偏倚风险较高，且研究的受试者与预测因子相关信息表述不清晰。研究普遍存在报告不充分、验证样本量有限的问题。结论——将AI应用于组织病理学图像以实现卵巢癌诊断或预后的研究仍较为有限，且尚无相关模型被证明可用于实际临床部署。保障临床转化的关键要点包括：更透明全面地报告数据来源与建模方法，以及通过交叉验证和外部验证改进定量性能评估。
英文摘要：
Purpose - To characterise and assess the quality of published research evaluating artificial intelligence (AI) methods for ovarian cancer diagnosis or prognosis using histopathology data. Methods - A search of PubMed, Scopus, Web of Science, CENTRAL, and WHO-ICTRP was conducted up to 19/05/2023. The inclusion criteria required that research evaluated AI on histopathology images for diagnostic or prognostic inferences in ovarian cancer. The risk of bias was assessed using PROBAST. Information about each model of interest was tabulated and summary statistics were reported. PRISMA 2020 reporting guidelines were followed. Results - 1573 records were identified, of which 45 were eligible for inclusion. There were 80 models of interest, including 37 diagnostic models, 22 prognostic models, and 21 models with other diagnostically relevant outcomes. Models were developed using 1-1375 slides from 1-776 ovarian cancer patients. Model outcomes included treatment response (11/80), malignancy status (10/80), stain quantity (9/80), and histological subtype (7/80). All models were found to be at high or unclear risk of bias overall, with most research having a high risk of bias in the analysis and a lack of clarity regarding participants and predictors in the study. Research frequently suffered from insufficient reporting and limited validation using small sample sizes. Conclusion - Limited research has been conducted on the application of AI to histopathology images for diagnostic or prognostic purposes in ovarian cancer, and none of the associated models have been demonstrated to be ready for real-world implementation. Key aspects to help ensure clinical translation include more transparent and comprehensive reporting of data provenance and modelling approaches, as well as improved quantitative performance evaluation using cross-validation and external validations.
Benchmarking Histopathology Foundation Models for Ovarian Cancer Bevacizumab Treatment Response Prediction from Whole Slide Images
Faculty of Science, University of British Columbia（不列颠哥伦比亚大学理学院）；School of Biomedical Engineering, University of British Columbia（不列颠哥伦比亚大学生物医学工程学院）；Department of Pathology and Laboratory Medicine, University of British Columbia（不列颠哥伦比亚大学病理学与实验医学系）；Canada’s Michael Smith Genome Sciences Centre, BC Cancer Research Institute（加拿大迈克尔·史密斯基因组科学中心，BC癌症研究所）
A Comprehensive Evaluation of Histopathology Foundation Models for Ovarian Cancer Subtype Classification
University of Leeds（利兹大学）；Leeds Institute of Medical Research at St James's（圣詹姆斯利兹医学研究所）；Leeds Cancer Centre（利兹癌症中心）；St James's University Hospital（圣詹姆斯大学医院）；Leeds Teaching Hospitals NHS Trust（利兹教学医院国民保健信托基金）；National Pathology Imaging Cooperative (NPIC)（国家病理影像合作组织）
University of Leeds（利兹大学）；Leeds Teaching Hospitals NHS Trust（利兹教学医院NHS信托基金会）
Weakly-supervised classification of histopathology slides is a computationally intensive task, with a typical whole slide image (WSI) containing billions of pixels to process. We propose Discriminative Region Active Sampling for Multiple Instance Learning (DRAS-MIL), a computationally efficient slide classification method using attention scores to focus sampling on highly discriminative regions. We apply this to the diagnosis of ovarian cancer histological subtypes, which is an essential part of the patient care pathway as different subtypes have different genetic and molecular profiles, treat…
Khulna University of Engineering & Technology（库尔纳工程技术大学）
Ovarian Cancer (OC) is type of female reproductive malignancy which can be found among young girls and mostly the women in their fertile or reproductive. There are few number of cysts are dangerous and may it cause cancer. So, it is very important to predict and it can be from different types of screening are used for this detection using Transvaginal Ultrasonography (TVUS) screening. In this research, we employed an actual datasets called PLCO with TVUS screening and three machine learning (ML) techniques, respectively Random Forest KNN, and XGBoost within three target variables. We obtained …
Tianjin University（天津大学）；CSIRO Data61（澳大利亚联邦科学与工业研究组织Data61）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Shandong University（山东大学）
Ovarian cancer is one of the most serious cancers that threaten women around the world. Epithelial ovarian cancer (EOC), as the most commonly seen subtype of ovarian cancer, has rather high mortality rate and poor prognosis among various gynecological cancers. Survival analysis outcome is able to provide treatment advices to doctors. In recent years, with the development of medical imaging technology, survival prediction approaches based on pathological images have been proposed. In this study, we designed a deep framework named EOCSA which analyzes the prognosis of EOC patients based on patho…
Serous borderline ovarian tumor (SBOT) and high-grade serous ovarian cancer (HGSOC) are two distinct subtypes of epithelial ovarian tumors, with markedly different biologic background, behavior, prognosis, and treatment. However, the histologic diagnosis of serous ovarian tumors can be subjectively variable and labor-intensive as multiple tumor slides/blocks need to be thoroughly examined to search for these features. In this study, we aimed to evaluate technical feasibility of using digital pathological approaches to facilitate objective and scalable diagnosis screening for SBOT and HGSOC. Ba…
Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping
Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)（生物医学计算成像与模拟技术中心）；School of Computing, University of Leeds（利兹大学计算机学院）；Leeds Institute of Medical Research at St James’s, School of Medicine, University of Leeds（利兹大学医学院圣詹姆斯利兹医学研究所）；Leeds Cancer Centre, St James’s University Hospital（圣詹姆斯大学医院利兹癌症中心）
Efficient Breast and Ovarian Cancer Classification via ViT-Based Preprocessing and Transfer Learning
Department of Mathematics, University of Texas at Arlington（德克萨斯大学阿灵顿分校数学系）；Department of Data Science and Mathematics, Embry-Riddle Aeronautical University（埃姆伯里-瑞德航空大学数据科学与数学系）
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；University of Ulsan College of Medicine（蔚山大学医学院）；Asan Medical Center（峨山医学中心）；Ajou University School of Medicine（亚洲大学医学院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
Deep learning has been widely used to analyze digitized hematoxylin and eosin (H&E)-stained histopathology whole slide images. Automated cancer segmentation using deep learning can be used to diagnose malignancy and to find novel morphological patterns to predict molecular subtypes. To train pixel-wise cancer segmentation models, manual annotation from pathologists is generally a bottleneck due to its time-consuming nature. In this paper, we propose Deep Interactive Learning with a pretrained segmentation model from a different cancer type to reduce manual annotation time. Instead of annotatin…
University of British Columbia（不列颠哥伦比亚大学）；Vancouver General Hospital（温哥华综合医院）；BC Cancer Research Center（不列颠哥伦比亚癌症研究中心）
Ovarian cancer is the most lethal cancer of the female reproductive organs. There are $5$ major histological subtypes of epithelial ovarian cancer, each with distinct morphological, genetic, and clinical features. Currently, these histotypes are determined by a pathologist's microscopic examination of tumor whole-slide images (WSI). This process has been hampered by poor inter-observer agreement (Cohen's kappa $0.54$-$0.67$). We utilized a \textit{two}-stage deep transfer learning algorithm based on convolutional neural networks (CNN) and progressive resizing for automatic classification of ep…
University of British Columbia（不列颠哥伦比亚大学）；BC Cancer Agency（不列颠哥伦比亚省癌症局）；Deeley Research Centre（迪利研究中心）
In clinical practice, many diagnosis tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques require labels, providing manual cell annotations is time-consuming due to the large number of cells. In this paper, we propose a self-supervised framework (VOLTA) for cell representation learning in histopathology images using a novel technique that accounts for the cell's mutual relationship with its environment for improved cell representations. We subjected our model to extensive experiments on the data collected from multiple institutions ar…
An attention-based deep learning network for predicting Platinum resistance in ovarian cancer
College of Medicine and Biological Information Engineering, Northeastern University（东北大学医学与生物信息工程学院）；Shengjing Hospital of China Medical University（中国医科大学附属盛京医院）
DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology
University of Padova, Italy（意大利帕多瓦大学）；Unit of Surgical Pathology and Cytopathology, Ospedale dell’Angelo, Italy（意大利奥斯佩达莱-安吉洛外科病理学与细胞病理学单位）；Pathological Anatomy Unit, Padova University-Hospital, Italy（意大利帕多瓦大学医院病理学解剖单位）；Department of Medicine, University of Padova, Italy（意大利帕多瓦大学医学系）；Department of Integrated Diagnostics, Azienda Ospedale-Università Padova, Italy（意大利帕多瓦医院-大学整合诊断系）
Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer
Southern University of Science and Technology（南方科技大学）；University of Science and Technology of China（中国科学技术大学）；the First Affiliated Hospital of USTC（中国科学技术大学附属第一医院）
Magnetic resonance imaging (MRI) has great potential to improve prostate cancer diagnosis. It can spare men with a normal exam from undergoing invasive biopsy while making biopsies more accurate in men with lesions suspicious for cancer. Yet, the subtle differences between cancer and confounding conditions, render the interpretation of MRI challenging. The tissue collected from patients that undergo pre-surgical MRI and radical prostatectomy provides a unique opportunity to correlate histopathology images of the entire prostate with MRI in order to accurately map the extent of prostate cancer …
Current methods for diagnosing the progression of multiple types of cancer within patients rely on interpreting stained needle biopsies. This process is time-consuming and susceptible to error throughout the paraffinization, Hematoxylin and Eosin (H&E) staining, deparaffinization, and annotation stages. Fourier Transform Infrared (FTIR) imaging has been shown to be a promising alternative to staining for appropriately annotating biopsy cores without the need for deparaffinization or H&E staining with the use of Fourier Transform Infrared (FTIR) images when combined with machine learning to int…
University of Toronto（多伦多大学）；National Cancer Research Centre（国家癌症研究中心）；Institute for Oncology and Radiology（肿瘤与放射学研究所）
Accurate diagnosis of breast cancer in histopathology images is challenging due to the heterogeneity of cancer cell growth as well as of a variety of benign breast tissue proliferative lesions. In this paper, we propose a practical and self-interpretable invasive cancer diagnosis solution. With minimum annotation information, the proposed method mines contrast patterns between normal and malignant images in unsupervised manner and generates a probability map of abnormalities to verify its reasoning. Particularly, a fully convolutional autoencoder is used to learn the dominant structural patter…
Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI suffers from high inter-observer variability across radiologists, thereby contributing to missed clinically significant cancers, overdiagnosed low-risk cancers, and frequent false positives. Interpretation of MRI could be greatly improved by providing radiologists with an answer key that clearly shows cancer locations on MRI. Registration of histopathology images from patients who had radical prostatectomy to pre-operative MRI allows such mapping…
Polytechnique Montreal（蒙特利尔理工学院）；Centre de recherche du CHUM (CRCHUM)（蒙特利尔大学医院研究中心（CRCHUM））
Breast cancer is the most diagnosed cancer and the most predominant cause of death in women worldwide. Imaging techniques such as the breast cancer pathology helps in the diagnosis and monitoring of the disease. However identification of malignant cells can be challenging given the high heterogeneity in tissue absorbotion from staining agents. In this work, we present a novel approach for Invasive Ductal Carcinoma (IDC) cells discrimination in histopathology slides. We propose a model derived from the Inception architecture, proposing a multi-level batch normalization module between each convo…
Jalpaiguri Government Engineering College（贾尔派古里政府工程学院）
Invasive ductal carcinoma (IDC), which is also sometimes known as the infiltrating ductal carcinoma, is the most regular form of breast cancer. It accounts for about 80% of all breast cancers. According to the American Cancer Society, more than 180,000 women in the United States are diagnosed with invasive breast cancer each year. The survival rate associated with this form of cancer is about 77% to 93% depending on the stage at which they are being diagnosed. The invasiveness and the frequency of the occurrence of these disease makes it one of the difficult cancers to be diagnosed. Our propos…
Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer
Hunan University（湖南大学）；The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）；Central South University（中南大学）；Hunan Clinical Medical Research Center for Cancer Pathogenic Genes Testing and Diagnosis（湖南省肿瘤致病基因检测与诊断临床医学研究中心）；Shanghai Cancer Institute（上海市肿瘤研究所）；Renji Hospital, School of Medicine, Shanghai Jiaotong University（上海交通大学医学院附属仁济医院）；Shanghai Jiaotong University（上海交通大学）
University of Waterloo（滑铁卢大学）；Vector Institute（向量研究所）
Representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology. We propose a two-stage framework for WSI representation learning. We sample relevant patches using a color-based method and use graph neural networks to learn relations among sampled patches to aggregate the image information into a single vector representation. We introduce attention via graph pooling to automatically infer patches with higher relevance. We demonstrate the performance of our approach for discriminating two sub-types of lung ca…
Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology
University of Oslo（奥斯陆大学）；Otto-von-Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；Singapore Institute of Technology（新加坡科技学院）；Oslo University Hospital（奥斯陆大学医院）；UiT The Arctic University of Norway（挪威北极大学）
Cancer is the uncontrollable cell division of abnormal cells inside the human body, which can spread to other body organs. It is one of the non-communicable diseases (NCDs) and NCDs accounts for 71% of total deaths worldwide whereas lung cancer is the second most diagnosed cancer after female breast cancer. Cancer survival rate of lung cancer is only 19%. There are various methods for the diagnosis of lung cancer, such as X-ray, CT scan, PET-CT scan, bronchoscopy and biopsy. However, to know the subtype of lung cancer based on the tissue type H and E staining is widely used, where the staining…
University of Connecticut（康涅狄格大学）；University of Connecticut Health Center（康涅狄格大学健康中心）
Prostate cancer is the most common cancer in men worldwide and the second leading cause of cancer death in the United States. One of the prognostic features in prostate cancer is the Gleason grading of histopathology images. The Gleason grade is assigned based on tumor architecture on Hematoxylin and Eosin (H&E) stained whole slide images (WSI) by the pathologists. This process is time-consuming and has known interobserver variability. In the past few years, deep learning algorithms have been used to analyze histopathology images, delivering promising results for grading prostate cancer. Howev…
Google Health（谷歌健康）；Advanced Clinical；Tempus Labs Inc（Tempus实验室公司）
Prognostic information at diagnosis has important implications for cancer treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic insights, improving risk stratification remains an active research area. We developed a deep learning system (DLS) to predict disease specific survival across 10 cancer types from The Cancer Genome Atlas (TCGA). We used a weakly-supervised approach without pixel-level annotations, and tested three different survival loss functions. The DLS was developed using 9,086 slid…
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…
Artificial Intelligence for the Assessment of Peritoneal Carcinosis during Diagnostic Laparoscopy for Advanced Ovarian Cancer
IRCAD, Research Institute against Digestive Cancer, Strasbourg, France（IRCAD消化道癌症研究机构，法国斯特拉斯堡）；University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France（斯特拉斯堡大学，法国）；Bioimage Analysis Center, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy（生物图像分析中心，意大利罗马）
Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；University of Utah（犹他大学）
Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides. The cancer's severity and risk of metastasis are determined by the Gleason grade, a score based on the organization and morphology of prostate cancer glands. For diagnostic work-up, pathologists first locate glands in the whole biopsy core, and -- if they detect cancer -- they assign a Gleason grade. This time-consuming process is subject to errors and significant inter-observer variability, despite strict diagnostic criteria. This paper proposes an automated workflow that follows patholog…
Indian Institute of Technology Bombay（印度理工学院孟买分校）；University of Illinois（伊利诺伊大学）
Spatial arrangement of cells of various types, such as tumor infiltrating lymphocytes and the advancing edge of a tumor, are important features for detecting and characterizing cancers. However, convolutional neural networks (CNNs) do not explicitly extract intricate features of the spatial arrangements of the cells from histopathology images. In this work, we propose to classify cancers using graph convolutional networks (GCNs) by modeling a tissue section as a multi-attributed spatial graph of its constituent cells. Cells are detected using their nuclei in H&E stained tissue image, and each …
Faculty of Sciences（理学院）；Mohammed V University（穆罕默德五世大学）；École Normale Supérieure（高等师范学院）
Cervical cancer will cause 460 000 deaths per year by 2040, approximately 90% are Sub-Saharan African women. A constantly increasing incidence in Africa making cervical cancer a priority by the World Health Organization (WHO) in terms of screening, diagnosis, and treatment. Conventionally, cancer diagnosis relies primarily on histopathological assessment, a deeply error-prone procedure requiring intelligent computer-aided systems as low-cost patient safety mechanisms but lack of labeled data in digital pathology limits their applicability. In this study, few cervical tissue digital slides from…
Technological University Dublin（都柏林理工大学）；University College Dublin（都柏林大学学院）；St. James Hospital（圣詹姆斯医院）；Trinity College Dublin（都柏林三一学院）；Beaumont Hospital（博蒙特医院）
The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained…
University of Warwick（华威大学）；Tissue Image Analytics (TIA) Centre, University of Warwick（华威大学组织图像分析中心）
The problem of recognizing various types of tissues present in multi-gigapixel histology images is an important fundamental pre-requisite for downstream analysis of the tumor microenvironment in a bottom-up analysis paradigm for computational pathology. In this paper, we propose a deep dictionary learning approach to solve the problem of tissue phenotyping in histology images. We propose deep Multi-Resolution Dictionary Learning (deepMRDL) in order to benefit from deep texture descriptors at multiple different spatial resolutions. We show the efficacy of the proposed approach through extensive…
The distribution and appearance of nuclei are essential markers for the diagnosis and study of cancer. Despite the importance of nuclear morphology, there is a lack of large scale, accurate, publicly accessible nucleus segmentation data. To address this, we developed an analysis pipeline that segments nuclei in whole slide tissue images from multiple cancer types with a quality control process. We have generated nucleus segmentation results in 5,060 Whole Slide Tissue images from 10 cancer types in The Cancer Genome Atlas. One key component of our work is that we carried out a multi-level qual…
University of California, San Francisco（加州大学旧金山分校）；Duke University（杜克大学）；University of Dusseldorf（杜塞尔多夫大学）；University of Washington（华盛顿大学）
Non-invasive prostate cancer detection from MRI has the potential to revolutionize patient care by providing early detection of clinically-significant disease (ISUP grade group >= 2), but has thus far shown limited positive predictive value. To address this, we present an MRI-based deep learning method for predicting clinically significant prostate cancer applicable to a patient population with subsequent ground truth biopsy results ranging from benign pathology to ISUP grade group~5. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classi…
The interpretation of prostate MRI suffers from low agreement across radiologists due to the subtle differences between cancer and normal tissue. Image registration addresses this issue by accurately mapping the ground-truth cancer labels from surgical histopathology images onto MRI. Cancer labels achieved by image registration can be used to improve radiologists' interpretation of MRI by training deep learning models for early detection of prostate cancer. A major limitation of current automated registration approaches is that they require manual prostate segmentations, which is a time-consum…
Stony Brook Medicine（石溪大学医学中心）；Stony Brook University（石溪大学）；Cold Spring Harbor Laboratory（冷泉港实验室）
Histopathology remains the gold standard for diagnosis of various cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for various tasks, including immune cell detection and microsatellite instability classification. The state-of-the-art for each task often employs base architectures that have been pretrained for image classification on ImageNet. The standard approach to develop classifiers in histopathology tends to focus narrowly on optimizing models for a single task, not considering the aspects of modeling innovatio…
Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. To shed light on this problem, we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease. First, we find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that differentiates critical structures of thin tissue sections with subcellular spatial resolution, including nuclei, cytoplasm, stroma, basal cells, nerves…
Karolinska Institutet（卡罗林斯卡学院）；Tampere University（坦佩雷大学）；Lund University（隆德大学）；University of Turku（图尔库大学）；Karolinska University Laboratory（卡罗林斯卡大学实验室）
Molecular phenotyping is central in cancer precision medicine, but remains costly and standard methods only provide a tumour average profile. Microscopic morphological patterns observable in histopathology sections from tumours are determined by the underlying molecular phenotype and associated with clinical factors. The relationship between morphology and molecular phenotype has a potential to be exploited for prediction of the molecular phenotype from the morphology visible in histopathology images. We report the first transcriptome-wide Expression-MOrphology (EMO) analysis in breast cancer,…
College of Aerospace Science and Engineering, National University of Defense Technology（国防科技大学航空航天科学与工程学院）；Eindhoven University of Technology（埃因霍温理工大学）；ScreenPoint Medical（ScreenPoint Medical 公司）；Pingan Technology（平安科技）；Lunit Inc.（Lunit 公司）；Queen Mary University of London（伦敦玛丽女王大学）；Beihang University（北京航空航天大学）；Beijing Advanced Innovation Center for Biomedical Engineering（北京生物医学工程高精尖创新中心）
Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submi…
We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from H&E stain to PHH3 and vice versa. We use the resulting synthetic images to build Convolutional Neural Networks (CNN) for automatic detection of mitotic figures, a strong prognostic biomarker used in routine breast cancer diagnosis and grading. We propose several scenarios, in which CNN trained with synthetically generated histopathology images perform on par with or even better than the same baseline model trained with real images. We discuss the potentia…
A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences…
Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham（阿姆里塔·维什瓦·维迪亚皮塔姆大学无线网络与应用中心）；Valeo（法雷奥）；Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham（阿姆里塔·维什瓦·维迪亚皮塔姆大学计算机科学与工程系）；School of Computing and Electrical Engineering, IIT Mandi（印度理工学院曼迪分校计算与电气工程学院）
Computer assisted diagnosis in digital pathology is becoming ubiquitous as it can provide more efficient and objective healthcare diagnostics. Recent advances have shown that the convolutional Neural Network (CNN) architectures, a well-established deep learning paradigm, can be used to design a Computer Aided Diagnostic (CAD) System for breast cancer detection. However, the challenges due to stain variability and the effect of stain normalization with such deep learning frameworks are yet to be well explored. Moreover, performance analysis with arguably more efficient network models, which may…
