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）
Prostate cancer ranks among the leading health issues impacting men, with the Gleason scoring system serving as the primary method for diagnosis and prognosis. This system relies on expert pathologists to evaluate samples of prostate tissue and assign a Gleason grade, a task that requires significant time and manual effort. To address this challenge, artificial intelligence (AI) solutions have been explored to automate the grading process. In light of these challenges, this study evaluates and compares the effectiveness of three deep learning methodologies, YOLO, Vision Transformers, and Vision Mamba, in accurately classifying Gleason grades from histopathology images. The goal is to enhance diagnostic precision and efficiency in prostate cancer management. This study utilized two publicly available datasets, Gleason2019 and SICAPv2, to train and test the performance of YOLO, Vision Transformers, and Vision Mamba models. Each model was assessed based on its ability to classify Gleason grades accurately, considering metrics such as false positive rate, false negative rate, precision, and recall. The study also examined the computational efficiency and applicability of each method in a clinical setting. Vision Mamba demonstrated superior performance across all metrics, achieving high precision and recall rates while minimizing false positives and negatives. YOLO showed promise in terms of speed and efficiency, particularly beneficial for real-time analysis. Vision Transformers excelled in capturing long-range dependencies within images, although they presented higher computational complexity compared to the other models. Vision Mamba emerges as the most effective model for Gleason grade classification in histopathology images, offering a balance between accuracy and computational efficiency.
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…
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）
The Gleason grading system using histological images is the most powerful diagnostic and prognostic predictor of prostate cancer. The current standard inspection is evaluating Gleason H&E-stained histopathology images by pathologists. However, it is complicated, time-consuming, and subject to observers. Deep learning (DL) based-methods that automatically learn image features and achieve higher generalization ability have attracted significant attention. However, challenges remain especially using DL to train the whole slide image (WSI), a predominant clinical source in the current diagnostic s…
Automated grading of prostate cancer histopathology images is a challenging task, with one key challenge being the scarcity of annotations down to the level of regions of interest (strong labels), as typically the prostate cancer Gleason score is known only for entire tissue slides (weak labels). In this study, we focus on automated Gleason score assignment of prostate cancer whole-slide images on the basis of a large weakly-labeled dataset and a smaller strongly-labeled one. We efficiently leverage information from both label sources by jointly training a classifier on the two datasets and by…
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…
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）
Top Data Science Ltd.（顶级数据科学有限公司）；University of Helsinki（赫尔辛基大学）；Skåne University Hospital（斯科讷大学医院）；Helsinki University Hospital（赫尔辛基大学医院）
Deep neural networks have introduced significant advancements in the field of machine learning-based analysis of digital pathology images including prostate tissue images. With the help of transfer learning, classification and segmentation performance of neural network models have been further increased. However, due to the absence of large, extensively annotated, publicly available prostate histopathology datasets, several previous studies employ datasets from well-studied computer vision tasks such as ImageNet dataset. In this work, we propose a transfer learning scheme from breast histopath…
Magnetic Resonance Imaging (MRI) is widely used for screening and staging prostate cancer. However, many prostate cancers have subtle features which are not easily identifiable on MRI, resulting in missed diagnoses and alarming variability in radiologist interpretation. Machine learning models have been developed in an effort to improve cancer identification, but current models localize cancer using MRI-derived features, while failing to consider the disease pathology characteristics observed on resected tissue. In this paper, we propose CorrSigNet, an automated two-step model that localizes p…
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…
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…
Stony Brook University（石溪大学）；Brigham and Women's Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Fraunhofer Institute for Medical Image Computing MEVIS（弗劳恩霍夫医学图像计算研究所）；University of Bremen（不来梅大学）
Histopathological assessments, including surgical resection and core needle biopsy, are the standard procedures in the diagnosis of the prostate cancer. Current interpretation of the histopathology images includes the determination of the tumor area, Gleason grading, and identification of certain prognosis-critical features. Such a process is not only tedious, but also prune to intra/inter-observe variabilities. Recently, FDA cleared the marketing of the first whole slide imaging system for digital pathology. This opens a new era for the computer aided prostate image analysis and feature extra…
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；College of Biomedical Engineering, Sichuan University（四川大学生物医学工程学院）；AI Lab, Tencent（腾讯AI实验室）；The Fourth Hospital of Hebei Medical University（河北医科大学第四医院）；Massachusetts General Hospital（麻省总医院）；Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）；Harvard Medical School（哈佛医学院）；Children’s Hospital of Chongqing Medical University（重庆医科大学附属儿童医院）；The Affiliated Yantai Yuhuangding Hospital of Qingdao University（青岛大学附属烟台毓璜顶医院）
Facebook（脸书）；Stanford University School of Medicine（斯坦福大学医学院）；Stanford University（斯坦福大学）；VA Palo Alto Health Care System（帕洛阿尔托退伍军人医疗保健系统）
Prostate cancer is the second deadliest cancer for American men. While Magnetic Resonance Imaging (MRI) is increasingly used to guide targeted biopsies for prostate cancer diagnosis, its utility remains limited due to high rates of false positives and false negatives as well as low inter-reader agreements. Machine learning methods to detect and localize cancer on prostate MRI can help standardize radiologist interpretations. However, existing machine learning methods vary not only in model architecture, but also in the ground truth labeling strategies used for model training. In this study, we…
University of California, San Francisco（加州大学旧金山分校）；Duke University（杜克大学）
Early prostate cancer detection and staging from MRI are extremely challenging tasks for both radiologists and deep learning algorithms, but the potential to learn from large and diverse datasets remains a promising avenue to increase their generalization capability both within- and across clinics. To enable this for prototype-stage algorithms, where the majority of existing research remains, in this paper we introduce a flexible federated learning framework for cross-site training, validation, and evaluation of deep prostate cancer detection algorithms. Our approach utilizes an abstracted rep…
Poisson Ordinal Network for Gleason Group Estimation Using Bi-Parametric MRI
Centre for Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Centre for Medical Imaging, University College London（伦敦大学学院医学影像中心）；National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）
Weakly Supervised Spatial Grounding for Discriminative Attention-Based Ultrasound-Histopathology Alignment in Prostate Cancer Grading
University of British Columbia（不列颠哥伦比亚大学）；Vector Institute（向量研究所）；Ordensklinikum Linz（林茨教团医院）；University of Alberta（阿尔伯塔大学）；Exact Imaging（Exact Imaging公司）
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…
KOAL: Knowledge-Driven Prostate Cancer Grading with Ordinal-Aware Learning
School of Computer Science, Sichuan University（四川大学计算机学院）；China Mobile (Suzhou) Software Technology Company Limited（中国移动（苏州）软件技术有限公司）；Shanghai Changhai Hospital, Naval Medical University（上海长海医院，海军医学大学）；Renji Hospital, Shanghai Jiao Tong University School of Medicine（仁济医院，上海交通大学医学院）
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…
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）；The Netherlands Cancer Institute, Antoni van Leeuwenhoek Hospital (NKI-AVL)（荷兰癌症研究所-安东尼·范·列文虎克医院（NKI-AVL））；Laboratory of Pathology East Netherlands (LabPON)（荷兰东部病理实验室（LabPON））
The Gleason score is the most important prognostic marker for prostate cancer patients but suffers from significant inter-observer variability. We developed a fully automated deep learning system to grade prostate biopsies. The system was developed using 5834 biopsies from 1243 patients. A semi-automatic labeling technique was used to circumvent the need for full manual annotation by pathologists. The developed system achieved a high agreement with the reference standard. In a separate observer experiment, the deep learning system outperformed 10 out of 15 pathologists. The system has the pote…
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…
Pathologist Attention-Aligned Report Generation for Prostate Histopathology
Department of Computer Science, Stony Brook University（纽约州立大学石溪分校计算机科学系）；Department of Biomedical Informatics, Stony Brook University（纽约州立大学石溪分校生物医学信息学系）；Université Paris-Saclay, CentraleSupélec, Gustave Roussy, INSERM, IHU PRISM, Cancer Data Science Unit（巴黎萨克雷大学、中央理工高等电力学院、古斯塔夫·鲁西研究所、法国国家健康与医学研究院、PRISM综合大学医院、癌症数据科学单元）；Université Paris-Saclay, CentraleSupélec, MICS Laboratory（巴黎萨克雷大学、中央理工高等电力学院、MICS实验室）；Department of Pathology and Laboratory Medicine, Northwell Health Laboratories（诺斯韦尔健康实验室病理与检验医学部）；Department of Pathology, University of Utah School of Medicine（犹他大学医学院病理系）；Department of Psychology, Stony Brook University（纽约州立大学石溪分校心理学系）
Prostate Age Gap (PAG): An MRI surrogate marker of aging for prostate cancer detection
University of Stavanger（斯塔万格大学）；Stavanger University Hospital（斯塔万格大学医院）；Karolinska Institutet（卡罗林斯卡学院）；Capio S:t Gorän Hospital（卡皮奥圣戈兰医院）；Danderyd Hospital（丹德里德医院）
Scaling Artificial Intelligence for Prostate Cancer Detection on MRI towards Organized Screening and Primary Diagnosis in a Global, Multiethnic Population (Study Protocol)
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）
Queen’s University（女王大学）；University of British Columbia（不列颠哥伦比亚大学）；Exact Imaging
MOTIVATION: Detection of prostate cancer during transrectal ultrasound-guided biopsy is challenging. The highly heterogeneous appearance of cancer, presence of ultrasound artefacts, and noise all contribute to these difficulties. Recent advancements in high-frequency ultrasound imaging - micro-ultrasound - have drastically increased the capability of tissue imaging at high resolution. Our aim is to investigate the development of a robust deep learning model specifically for micro-ultrasound-guided prostate cancer biopsy. For the model to be clinically adopted, a key challenge is to design a so…
The automated segmentation of cancer tissue in histopathology images can help clinicians to detect, diagnose, and analyze such disease. Different from other natural images used in many convolutional networks for benchmark, histopathology images can be extremely large, and the cancerous patterns can reach beyond 1000 pixels. Therefore, the well-known networks in the literature were never conceived to handle these peculiarities. In this work, we propose a Fully Convolutional DenseUNet that is particularly designed to solve histopathology problems. We evaluated our network in two public pathology…
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…
Segmentation Strategies in Deep Learning for Prostate Cancer Diagnosis: A Comparative Study of Mamba, SAM, and YOLO
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 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, 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与技术系）；Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science and Technology（伊朗科学技术大学电气工程学院生物医学工程系）
Purpose: We aimed to develop deep machine learning (DL) models to improve the detection and segmentation of intraprostatic lesions (IL) on bp-MRI by using whole amount prostatectomy specimen-based delineations. We also aimed to investigate whether transfer learning and self-training would improve results with small amount labelled data. Methods: 158 patients had suspicious lesions delineated on MRI based on bp-MRI, 64 patients had ILs delineated on MRI based on whole mount prostatectomy specimen sections, 40 patients were unlabelled. A non-local Mask R-CNN was proposed to improve the segmentat…
Queen’s University（女王大学）；University of British Columbia（不列颠哥伦比亚大学）；Exact Imaging（Exact Imaging公司）
A large body of previous machine learning methods for ultrasound-based prostate cancer detection classify small regions of interest (ROIs) of ultrasound signals that lie within a larger needle trace corresponding to a prostate tissue biopsy (called biopsy core). These ROI-scale models suffer from weak labeling as histopathology results available for biopsy cores only approximate the distribution of cancer in the ROIs. ROI-scale models do not take advantage of contextual information that are normally considered by pathologists, i.e. they do not consider information about surrounding tissue and …
East China Normal University（华东师范大学）；School of Computer Science, East China Normal University（华东师范大学计算机科学学院）
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 accor…
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…
Histopathological images provide rich information for disease diagnosis. Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra- observer agreement. Most previous work on whole slide image analysis has focused on classification or segmentation of small pre-selected regions-of-interest, which requires fine-grained annotation and is non-trivial to extend for large-scale whole slide analysis. In this paper,…
AI-assisted prostate cancer detection and localisation on biparametric MR by classifying radiologist-positives
Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London（医学图像计算中心，医学物理与生物医学工程系，伦敦大学学院）；Div of Surgery & Interventional Sci, University College London（外科与介入科学部，伦敦大学学院）；School of Computer Science and Engineering, University of Westminster（计算机科学与工程学院，威斯敏斯特大学）
University of California, Los Angeles（加州大学洛杉矶分校）；Cedars-Sinai Medical Center（西达赛奈医疗中心）
Histology review is often used as the `gold standard' for disease diagnosis. Computer aided diagnosis tools can potentially help improve current pathology workflows by reducing examination time and interobserver variability. Previous work in cancer grading has focused mainly on classifying pre-defined regions of interest (ROIs), or relied on large amounts of fine-grained labels. In this paper, we propose a two-stage attention-based multiple instance learning model for slide-level cancer grading and weakly-supervised ROI detection and demonstrate its use in prostate cancer. Compared with existi…
CHIMERA Challenge: Biochemical Recurrence Prediction in Prostate Cancer Patients using multimodal datasets
Radboud University Medical Center（拉德堡德大学医学中心）；Canisius Wilhelmina Hospital（卡尼修斯威廉明娜医院）；Oncode Institute（Oncode研究所）；Erasmus University Medical Center（伊拉斯姆斯大学医学中心）；Bydgoszcz University of Science and Technology（比得哥什科技大学）；Yerevan State Medical University（埃里温国立医科大学）；IMP Diagnostics（IMP诊断公司）
Maximizing T2-Only Prostate Cancer Localization from Expected Diffusion Weighted Imaging
UCL Hawkes Institute and the Department of Medical Physics and Biomedical Engineering, University College London（伦敦大学学院霍克斯研究所与医学物理与生物医学工程系）；Centre for Bioengineering, School of Engineering and Materials Science and Digital Environment Research Institute, Queen Mary University of London（伦敦玛丽女王大学工程与材料科学学院生物工程中心与数字环境研究所）；Centre for Medical Imaging, University College London（伦敦大学学院医学影像中心）；Division of Surgery & Interventional Science, University College London（伦敦大学学院外科与介入科学部）；Urological Research Network（泌尿研究网络）
The PAR dataset: Prostate biopsy whole slide images from an underrepresented Middle Eastern population
Koya University（科亚大学）；Karolinska Institutet（卡罗林斯卡学院）；Hawler Medical University（哈勒医科大学）；PAR Private Hospital（PAR私立医院）；University of Sulaimani（苏莱曼尼亚大学）；Stavanger University Hospital（斯塔万格大学医院）；University of Stavanger（斯塔万格大学）；ELLIS Institute Finland（芬兰ELLIS研究所）；University of Turku（图尔库大学）
Histopathological Spectrum-Guided Prostate Stratification via Segmentation-Assisted Diagnostic Transformer
College of Artificial Intelligence, Nankai University（南开大学人工智能学院）；Tianjin First Central Hospital（天津市第一中心医院）；School of Electronics and Information Engineering, Tiangong University（天津工业大学电子与信息工程学院）；School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）；North China Digital Health Technology Co., Ltd.（华北数字健康科技有限公司）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）
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,…
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 …
Google AI Healthcare（谷歌AI医疗保健）；Avoneaux Medical Institute（阿沃诺医疗研究所）；University of Tennessee Health Science Center（田纳西大学健康科学中心）
The increasing availability of large institutional and public histopathology image datasets is enabling the searching of these datasets for diagnosis, research, and education. Though these datasets typically have associated metadata such as diagnosis or clinical notes, even carefully curated datasets rarely contain annotations of the location of regions of interest on each image. Because pathology images are extremely large (up to 100,000 pixels in each dimension), further laborious visual search of each image may be needed to find the feature of interest. In this paper, we introduce a deep le…
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…
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…
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…
