Prostate cancer (PCa) is one of the most common cancers in men around the world. The most accurate method to evaluate lesion levels of PCa is microscopic inspection of stained biopsy tissue and estimate the Gleason score of tissue microarray (TMA) image by expert pathologists. However, it is time-consuming for pathologists to identify the cellular and glandular patterns for Gleason grading in large TMA images. We used Gleason2019 Challenge dataset to build a convolutional neural network (CNN) model to segment TMA images to regions of different Gleason grades and predict the Gleason score according to the grading segmentation. We used a pre-trained model of prostate segmentation to increase the accuracy of the Gleason grade segmentation. The model achieved a mean Dice of 75.6% on the test cohort and ranked 4th in the Gleason2019 Challenge with a score of 0.778 combined of Cohen's kappa and the f1-score.
Khalifa University（哈利法大学）；Beihang University（北京航空航天大学）；University of Louisville（路易斯维尔大学）
Prostate cancer (PCa) is the second deadliest form of cancer in males, and it can be clinically graded by examining the structural representations of Gleason tissues. This paper proposes \RV{a new method} for segmenting the Gleason tissues \RV{(patch-wise) in order to grade PCa from the whole slide images (WSI).} Also, the proposed approach encompasses two main contributions: 1) A synergy of hybrid dilation factors and hierarchical decomposition of latent space representation for effective Gleason tissues extraction, and 2) A three-tiered loss function which can penalize different semantic seg…
Worldwide, prostate cancer is one of the main cancers affecting men. The final diagnosis of prostate cancer is based on the visual detection of Gleason patterns in prostate biopsy by pathologists. Computer-aided-diagnosis systems allow to delineate and classify the cancerous patterns in the tissue via computer-vision algorithms in order to support the physicians' task. The methodological core of this work is a U-Net convolutional neural network for image segmentation modified with residual blocks able to segment cancerous tissue according to the full Gleason system. This model outperforms othe…
Prostate cancer is one of the main diseases affecting men worldwide. The Gleason scoring system is the primary diagnostic tool for prostate cancer. This is obtained via the visual analysis of cancerous patterns in prostate biopsies performed by expert pathologists, and the aggregation of the main Gleason grades in a combined score. Computer-aided diagnosis systems allow to reduce the workload of pathologists and increase the objectivity. Recently, efforts have been made in the literature to develop algorithms aiming the direct estimation of the global Gleason score at biopsy/core level with gl…
Inception Institute of Artificial Intelligence（起源人工智能研究院）
Gleason grading from histopathology images is essential for accurate prostate cancer (PCa) diagnosis. Since such images are obtained after invasive tissue resection quick diagnosis is challenging under the existing paradigm. We propose a method to predict Gleason grades from magnetic resonance (MR) images which are non-interventional and easily acquired. We solve the problem in a generalized zero-shot learning (GZSL) setting since we may not access training images of every disease grade. Synthetic MRI feature vectors of unseen grades (classes) are generated by exploiting Gleason grades' ordere…
Universitat Politècnica de València（瓦伦西亚理工大学）；ETS Montreal（蒙特利尔高等技术学院）
Prostate cancer is one of the main diseases affecting men worldwide. The gold standard for diagnosis and prognosis is the Gleason grading system. In this process, pathologists manually analyze prostate histology slides under microscope, in a high time-consuming and subjective task. In the last years, computer-aided-diagnosis (CAD) systems have emerged as a promising tool that could support pathologists in the daily clinical practice. Nevertheless, these systems are usually trained using tedious and prone-to-error pixel-level annotations of Gleason grades in the tissue. To alleviate the need of…
Inception Institute of Artificial Intelligence（人工智能初创研究院）
Segmentation of Prostate Cancer (PCa) tissues from Gleason graded histopathology images is vital for accurate diagnosis. Although deep learning (DL) based segmentation methods achieve state-of-the-art accuracy, they rely on large datasets with manual annotations. We propose a method to synthesize for PCa histopathology images by learning the geometrical relationship between different disease labels using self-supervised learning. We use a weakly supervised segmentation approach that uses Gleason score to segment the diseased regions and the resulting segmentation map is used to train a Shape R…
Universitat Politècnica de València（瓦伦西亚理工大学）；University Clinical Hospital of Valencia（瓦伦西亚大学临床医院）；University of Granada（格拉纳达大学）
The Gleason scoring system is the primary diagnostic and prognostic tool for prostate cancer. In recent years, with the development of digitisation devices, the use of computer vision techniques for the analysis of biopsies has increased. However, to the best of the authors' knowledge, the development of algorithms to automatically detect individual cribriform patterns belonging to Gleason grade 4 has not yet been studied in the literature. The objective of the work presented in this paper is to develop a deep-learning-based system able to support pathologists in the daily analysis of prostate…
Classification of Gleason Grading in Prostate Cancer Histopathology Images Using Deep Learning Techniques: YOLO, Vision Transformers, and Vision Mamba
School of Computer Engineering, Iran University of Science and Technology（伊朗科学技术大学计算机工程学院）；School of Electrical Engineering, Iran University of Science and Technology（伊朗科学技术大学电气工程学院）；School of Electrical Engineering, Shahrood Branch, Islamic Azad University（伊斯兰阿扎德大学沙赫鲁德分校电气工程学院）；Medical Sciences and Technologies Department, Science and Research Branch, Islamic Azad University（伊斯兰阿扎德大学科学研究分支医学科学与技术部门）
Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
University of Augsburg（奥格斯堡大学）；University Hospital Augsburg（奥格斯堡大学医院）；Neu-Ulm University of Applied Sciences（新乌尔姆应用科学大学）；Bavarian Cancer Research Center（巴伐利亚癌症研究中心）；BioM Biotech Cluster Development GmbH（BioM生物技术集群发展有限公司）
Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer
German Cancer Research Center (DKFZ)（德国癌症研究中心）；University Heidelberg（海德堡大学）；RWTH Aachen University（亚琛工业大学）；University of Ulm（乌尔姆大学）；University of Saarland（萨尔大学）；University Hospital Düsseldorf（杜塞尔多夫大学医院）；Medical University of Vienna（维也纳医科大学）；University Hospital Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医院）；Martin Luther University Halle-Wittenberg（马丁路德·哈勒维腾贝格大学）；University Hospital Zurich（苏黎世大学医院）；Städtisches Klinikum Karlsruhe（卡尔斯鲁厄市立医院）；University of Leipzig（莱比锡大学）；University Medical Center Freiburg（弗莱堡大学医学中心）
Radboud University Medical Center（拉德堡德大学医学中心）；Radboud Institute for Health Sciences（拉德堡德健康科学研究所）；University of Yaounde 1（雅温得第一大学）；Salomão Zoppi Diagnostics/DASA（萨洛芒·佐皮诊断中心/DASA）；Istanbul Medipol University（伊斯坦布尔 Medipol 大学）；Public Health Agency of Catalonia（加泰罗尼亚公共卫生署）；Centre de Pathologie（病理中心）；Hopital Diaconat Mulhouse（米卢斯迪亚科纳医院）；Aix en Provence Hospital（艾克斯-普罗旺斯医院）；Histo Patologia Cirúrgica e Citologia（组织外科病理与细胞学中心）；Tata Medical Center（塔塔医疗中心）；labor team w ag（W 实验室有限公司）；Instituto Mário Penna（马里奥·彭纳研究所）；Medics Pathologie（梅迪克病理中心）；Antoni van Leeuwenhoek Hospital（安东尼·范·列文虎克医院）；Netherlands Cancer Institute（荷兰癌症研究所）；Laboratory of Pathology East Netherlands（荷兰东部病理实验室）；Center for Medical Image Science and Visualization（医学影像科学与可视化中心）；Linköping University（林雪平大学）
While the Gleason score is the most important prognostic marker for prostate cancer patients, it suffers from significant observer variability. Artificial Intelligence (AI) systems, based on deep learning, have proven to achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance,…
DeepGleason: a System for Automated Gleason Grading of Prostate Cancer using Deep Neural Networks
University of Augsburg（奥格斯堡大学）；University Hospital Augsburg（奥格斯堡大学医院）；BioM Biotech Cluster Development GmbH（BioM生物技术集群发展有限公司）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）
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…
Brigham and Women's Hospital（布莱根和妇女医院）；Harvard Medical School（哈佛医学院）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）
Histology-based grade classification is clinically important for many cancer types in stratifying patients distinct treatment groups. In prostate cancer, the Gleason score is a grading system used to measure the aggressiveness of prostate cancer from the spatial organization of cells and the distribution of glands. However, the subjective interpretation of Gleason score often suffers from large interobserver and intraobserver variability. Previous work in deep learning-based objective Gleason grading requires manual pixel-level annotation. In this work, we propose a weakly-supervised approach …
GRAPHITE: Graph-Based Interpretable Tissue Examination for Enhanced Explainability in Breast Cancer Histopathology
School of Computer Science and Engineering（计算机科学与工程学院）；University of New South Wales（新南威尔士大学）；St. George and Sutherland Clinical School（圣乔治与萨瑟兰临床学院）；Department of Anatomical Pathology（解剖病理学部）；NSW Health Pathology（新南威尔士州卫生病理学）；Cancer Care Centre（癌症护理中心）
Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling
University of California, Los Angeles（加州大学洛杉矶分校）；California NanoSystems Institute（加州纳米系统研究所）；University of California at Davis（加州大学戴维斯分校）；Bnai-Zion Medical Center（贝内锡安医疗中心）；Hadassah Hebrew University Medical Center（哈达萨希伯来大学医疗中心）；David Geffen School of Medicine, University of California, Los Angeles（加州大学洛杉矶分校大卫·格芬医学院）
Institute of Molecular and Cell Biology（分子与细胞生物学研究所）；Bioinformatics Institute（生物信息学研究所）；National University of Singapore（新加坡国立大学）；University of Queensland（昆士兰大学）；Institute for Infocomm Research（资讯通信研究所）；Monash University Malaysia（莫纳什大学马来西亚分校）；Tan Tock Seng Hospital（陈笃生医院）；PanAsia Surgery Pte Ltd（泛亚外科私人有限公司）；Mount Elizabeth Novena Hospital（伊丽莎白诺维娜医院）；Surgi-TEN Specialists Pte Ltd（Surgi-TEN专科私人有限公司）；Farrer Park Hospital（花拉公园医院）；CNRS UMI 2955, Image & Pervasive Access Lab（法国国家科学研究中心UMI 2955，图像与普适访问实验室）；Singapore Eye Research Institute（新加坡眼科研究所）
Architecture, size, and shape of glands are most important patterns used by pathologists for assessment of cancer malignancy in prostate histopathological tissue slides. Varying structures of glands along with cumbersome manual observations may result in subjective and inconsistent assessment. Cribriform gland with irregular border is an important feature in Gleason pattern 4. We propose using deep neural networks for cribriform pattern classification in prostate histopathological images. $163708$ Hematoxylin and Eosin (H\&E) stained images were extracted from histopathologic tissue slides of …
Equivariant Imaging Biomarkers for Robust Unsupervised Segmentation of Histopathology
Department of Biomedical Engineering, Yale University（生物医学工程系，耶鲁大学）；Department of Radiology & Biomedical Imaging, Yale University（放射科与生物医学成像系，耶鲁大学）；Department of Urology, Yale University（泌尿外科系，耶鲁大学）；Medical Scientist Training Program, Yale University（医学科学家培训计划，耶鲁大学）
Stanford University School of Medicine（斯坦福大学医学院）；Stanford University（斯坦福大学）；Bonn University Hospital（波恩大学医院）；Institute for Pathology and Cytology（病理与细胞学研究所）；University Hospital Muenster（明斯特大学医院）
The current study detects different morphologies related to prostate pathology using deep learning models; these models were evaluated on 2,121 hematoxylin and eosin (H&E) stain histology images captured using bright field microscopy, which spanned a variety of image qualities, origins (whole slide, tissue micro array, whole mount, Internet), scanning machines, timestamps, H&E staining protocols, and institutions. For case usage, these models were applied for the annotation tasks in clinician-oriented pathology reports for prostatectomy specimens. The true positive rate (TPR) for slides with p…
Indian Institute of Technology, Bombay（印度理工学院孟买分校）
With the increase in the use of deep learning for computer-aided diagnosis in medical images, the criticism of the black-box nature of the deep learning models is also on the rise. The medical community needs interpretable models for both due diligence and advancing the understanding of disease and treatment mechanisms. In histology, in particular, while there is rich detail available at the cellular level and that of spatial relationships between cells, it is difficult to modify convolutional neural networks to point out the relevant visual features. We adopt an approach to model histology ti…
Karolinska Institutet（卡罗林斯卡医学院）；Tampere University（坦佩雷大学）；Uppsala University（乌普萨拉大学）；University of Otago（奥塔哥大学）；Queen Mary University of London（伦敦玛丽女王大学）；Bostwick Laboratories（博斯特威克实验室）；University Health Network（大学健康网络）；Indiana University School of Medicine（印第安纳大学医学院）；Yale University School of Medicine（耶鲁大学医学院）；Medical College of Wisconsin（威斯康星医学院）；Royal Prince Alfred Hospital（阿尔弗雷德王子医院）；University of Sydney（悉尼大学）；University Hospital Bonn（波恩大学医院）；University of São Paulo Medical School（圣保罗大学医学院）；Cleveland Clinic（克利夫兰医学中心）；Southmead Hospital（索斯米德医院）；Taipei Veterans General Hospital（台北荣民总医院）；Aquesta Uropathology（Aquesta泌尿病理中心）；University of Queensland（昆士兰大学）；University of Toronto（多伦多大学）；Jikei University School of Medicine（慈惠会医科大学）；Aichi Medical University（爱知医科大学）；University Hospital of Wales（威尔士大学医院）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）；SciLifeLab（生命科学实验室）；S:t Göran Hospital（圣约兰医院）
Background: An increasing volume of prostate biopsies and a world-wide shortage of uro-pathologists puts a strain on pathology departments. Additionally, the high intra- and inter-observer variability in grading can result in over- and undertreatment of prostate cancer. Artificial intelligence (AI) methods may alleviate these problems by assisting pathologists to reduce workload and harmonize grading. Methods: We digitized 6,682 needle biopsies from 976 participants in the population based STHLM3 diagnostic study to train deep neural networks for assessing prostate biopsies. The networks were …
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…
Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides
Norwegian University of Science and Technology (NTNU)（挪威科技大学）；St. Olavs hospital, Trondheim University Hospital（圣奥拉夫医院，特隆赫姆大学医院）；SINTEF Digital（SINTEF数字研究所）；Sopra Steria（索普瑞斯特利亚）；Uppsala University（乌普萨拉大学）；University of Bergen（卑尔根大学）；Haukeland University Hospital（豪克兰大学医院）
