Predicting Prostate Cancer-Specific Mortality with A.I.-based Gleason Grading
Google Health（谷歌健康）；Medical University of Graz（格拉茨医科大学）；Advanced Clinical；University of Tennessee Health Science Center（田纳西大学健康科学中心）；Tempus Labs Inc（Tempus Labs公司）
Ellery Wulczyn, Kunal Nagpal, Matthew Symonds, Melissa Moran, Markus Plass, Robert Reihs, Farah Nader, Fraser Tan, Yuannan Cai, Trissia Brown, Isabelle Flament-… 展开作者
Ellery Wulczyn, Kunal Nagpal, Matthew Symonds, Melissa Moran, Markus Plass, Robert Reihs, Farah Nader, Fraser Tan, Yuannan Cai, Trissia Brown, Isabelle Flament-Auvigne, Mahul B. Amin, Martin C. Stumpe, Heimo Muller, Peter Regitnig, Andreas Holzinger, Greg S. Corrado, Lily H. Peng, Po-Hsuan Cameron Chen, David F. Steiner, Kurt Zatloukal, Yun Liu, Craig H. Mermel
Gleason grading of prostate cancer is an important prognostic factor but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with expert pathologists, it remains an open question whether A.I. grading translates to better prognostication. In this study, we developed a system to predict prostate-cancer specific mortality via A.I.-based Gleason grading and subsequently evaluated its ability to risk-stratify patients on an independent retrospective cohort of 2,807 prostatectomy cases from a single European center with 5-25 years of follow-up (median: 13, interquartile range 9-17). The A.I.'s risk scores produced a C-index of 0.84 (95%CI 0.80-0.87) for prostate cancer-specific mortality. Upon discretizing these risk scores into risk groups analogous to pathologist Grade Groups (GG), the A.I. had a C-index of 0.82 (95%CI 0.78-0.85). On the subset of cases with a GG in the original pathology report (n=1,517), the A.I.'s C-indices were 0.87 and 0.85 for continuous and discrete grading, respectively, compared to 0.79 (95%CI 0.71-0.86) for GG obtained from the reports. These represent improvements of 0.08 (95%CI 0.01-0.15) and 0.07 (95%CI 0.00-0.14) respectively. Our results suggest that A.I.-based Gleason grading can lead to effective risk-stratification and warrants further evaluation for improving disease management.
Google AI Healthcare（谷歌人工智能医疗保健部门）；Naval Medical Center San Diego（圣地亚哥海军医疗中心）；Henry M. Jackson Foundation（亨利·M·杰克逊基金会）；University of Tennessee Health Science Center（田纳西大学健康科学中心）；University Health Network（大学健康网络）；University of Toronto（多伦多大学）；El Camino Hospital（埃尔卡米诺医院）
For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1,226 slides, and evaluated on an independent validation dataset of 331 slides, where the reference standard was established…
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）
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…
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…
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…
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…
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…
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…
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）
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）
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）
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…
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）
With the development of computational pathology, deep learning methods for Gleason grading through whole slide images (WSIs) have excellent prospects. Since the size of WSIs is extremely large, the image label usually contains only slide-level label or limited pixel-level labels. The current mainstream approach adopts multi-instance learning to predict Gleason grades. However, some methods only considering the slide-level label ignore the limited pixel-level labels containing rich local information. Furthermore, the method of additionally considering the pixel-level labels ignores the inaccura…
The emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a profound impact on the diagnosis of prostate cancers (PCa), which is the most prevalent malignancy in males in the western world, enabling a better selection of patients for confirmation biopsy. However, analyzing these images is complex even for experts, hence opening an opportunity for computer-aided diagnosis systems to seize. This paper proposes a fully automatic system based on Deep Learning that takes a prostate mpMRI from a PCa-suspect patient and, by leveraging the Retina U-Net detection framework, locates P…
University of Waterloo（滑铁卢大学）；Waterloo Artificial Intelligence Institute（滑铁卢人工智能研究所）；Lunenfeld-Tanenbaum Research Institute (LTRI)（卢嫩菲尔德-塔南鲍姆研究所（LTRI））；Sinai Health System（西奈卫生系统）；University of Toronto（多伦多大学）
Generative Adversarial Networks (GANs) have shown considerable promise for mitigating the challenge of data scarcity when building machine learning-driven analysis algorithms. Specifically, a number of studies have shown that GAN-based image synthesis for data augmentation can aid in improving classification accuracy in a number of medical image analysis tasks, such as brain and liver image analysis. However, the efficacy of leveraging GANs for tackling prostate cancer analysis has not been previously explored. Motivated by this, in this study we introduce ProstateGAN, a GAN-based model for sy…
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…
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…
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,…
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 …
Rutgers University（罗格斯大学）；Rutgers Cancer Institute of New Jersey（新泽西罗格斯癌症研究所）
Automatic and accurate Gleason grading of histopathology tissue slides is crucial for prostate cancer diagnosis, treatment, and prognosis. Usually, histopathology tissue slides from different institutions show heterogeneous appearances because of different tissue preparation and staining procedures, thus the predictable model learned from one domain may not be applicable to a new domain directly. Here we propose to adopt unsupervised domain adaptation to transfer the discriminative knowledge obtained from the source domain to the target domain without requiring labeling of images at the target…
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…
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…
Universitat Politècnica de València（瓦伦西亚理工大学）；University of Stavanger（斯塔万格大学）；Tyris Tech S.L.（泰里斯科技有限公司）
According to GLOBOCAN 2020, prostate cancer is the second most common cancer in men worldwide and the fourth most prevalent cancer overall. For pathologists, grading prostate cancer is challenging, especially when discriminating between Grade 3 (G3) and Grade 4 (G4). This paper proposes a Self-Supervised Learning (SSL) framework to classify prostate histopathological images when labeled images are scarce. In particular, a tailored Convolutional Auto Encoder (CAE) is trained to reconstruct 128x128x3 patches of prostate cancer Whole Slide Images (WSIs) as a pretext task. The downstream task of t…
Prostate cancer (PCa) is one of the most common and aggressive cancers worldwide. The Gleason score (GS) system is the standard way of classifying prostate cancer and the most reliable method to determine the severity and treatment to follow. The pathologist looks at the arrangement of cancer cells in the prostate and assigns a score on a scale that ranges from 6 to 10. Automatic analysis of prostate whole-slide images (WSIs) is usually addressed as a binary classification problem, which misses the finer distinction between stages given by the GS. This paper presents a probabilistic deep learn…
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…
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）
DISC: Latent Diffusion Models with Self-Distillation from Separated Conditions for Prostate Cancer Grading
Scientific Computing and Imaging Institute, University of Utah（犹他大学科学计算与成像研究所）；The Catholic University of Korea College of Medicine（韩国天主教大学医学院）；Case Western Reserve University（凯斯西储大学）；Huntsman Cancer Institute, University of Utah Health（犹他大学健康亨茨曼癌症研究所）；University of Utah（犹他大学）
Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet（卡罗林斯卡研究所医学流行病学与生物统计学系）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）
ContextVision AB（ContextVision公司）；Karolinska Institutet and University Hospital（卡罗林斯卡学院暨大学医院）
Gleason grading specified in ISUP 2014 is the clinical standard in staging prostate cancer and the most important part of the treatment decision. However, the grading is subjective and suffers from high intra and inter-user variability. To improve the consistency and objectivity in the grading, we introduced glandular tissue WithOut Basal cells (WOB) as the ground truth. The presence of basal cells is the most accepted biomarker for benign glandular tissue and the absence of basal cells is a strong indicator of acinar prostatic adenocarcinoma, the most common form of prostate cancer. Glandular…
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）
We propose an automated method for detecting aggressive prostate cancer(CaP) (Gleason score >=7) based on a comprehensive analysis of the lesion and the surrounding normal prostate tissue which has been simultaneously captured in T2-weighted MR images, diffusion-weighted images (DWI) and apparent diffusion coefficient maps (ADC). The proposed methodology was tested on a dataset of 79 patients (40 aggressive, 39 non-aggressive). We evaluated the performance of a wide range of popular quantitative imaging features on the characterization of aggressive versus non-aggressive CaP. We found that a g…
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）
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 …
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）
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…
An end-to-end-trained vision-language model for native-language prostate pathology report generation
Technical University of Munich（慕尼黑工业大学）；Munich Data Science Institute（慕尼黑数据科学研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）；University Hospital Heidelberg（海德堡大学医院）；Heidelberg University（海德堡大学）；Interdisciplinary Center for Scientific Computing (IWR)（跨学科科学计算中心（IWR））
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）
In this work, we propose a deep U-Net based model to tackle the challenging task of prostate cancer segmentation by aggressiveness in MRI based on weak scribble annotations. This model extends the size constraint loss proposed by Kervadec et al. 1 in the context of multiclass detection and segmentation task. This model is of high clinical interest as it allows training on prostate biopsy samples and avoids time-consuming full annotation process. Performance is assessed on a private dataset (219 patients) where the full ground truth is available as well as on the ProstateX-2 challenge database,…
Scaling Artificial Intelligence for Prostate Cancer Detection on MRI towards Organized Screening and Primary Diagnosis in a Global, Multiethnic Population (Study Protocol)
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…
Validation of Diagnostic Artificial Intelligence Models for Prostate Pathology in a Middle Eastern Cohort
Koya University（科亚大学）；Karolinska Institutet（卡罗林斯卡学院）；Hawler Medical University（霍勒医科大学）；PAR Private Hospital（PAR私立医院）；University of Sulaimani（苏莱曼尼大学）；Stavanger University Hospital（斯塔万格大学医院）；University of Stavanger（斯塔万格大学）
To our knowledge, all deep computer-aided detection and diagnosis (CAD) systems for prostate cancer (PCa) detection consider bi-parametric magnetic resonance imaging (bp-MRI) only, including T2w and ADC sequences while excluding the 4D perfusion sequence,which is however part of standard clinical protocols for this diagnostic task. In this paper, we question strategies to integrate information from perfusion imaging in deep neural architectures. To do so, we evaluate several ways to encode the perfusion information in a U-Net like architecture, also considering early versus mid fusion strategi…
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）
