Cross-Modal Self-Attention Distillation for Prostate Cancer Segmentation
University of Shanghai for Science and Technology（上海理工大学）；Tongji University（同济大学）；National University of Singapore（新加坡国立大学）；Shanghai Fire Corps Hospital（上海消防总队医院）；Shanghai Tenth People’s Hospital（上海市第十人民医院）
Guokai Zhang, Xiaoang Shen, Ye Luo, Jihao Luo, Zeju Wang, Weigang Wang, Binghui Zhao, Jianwei Lu
备注：
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Automatic segmentation of the prostate cancer from the multi-modal magnetic resonance images is of critical importance for the initial staging and prognosis of patients. However, how to use the multi-modal image features more efficiently is still a challenging problem in the field of medical image segmentation. In this paper, we develop a cross-modal self-attention distillation network by fully exploiting the encoded information of the intermediate layers from different modalities, and the extracted attention maps of different modalities enable the model to transfer the significant spatial information with more details. Moreover, a novel spatial correlated feature fusion module is further employed for learning more complementary correlation and non-linear information of different modality images. We evaluate our model in five-fold cross-validation on 358 MRI with biopsy confirmed. Extensive experiment results demonstrate that our proposed network achieves state-of-the-art performance.
Research Computing Center at the University of Chicago（芝加哥大学研究计算中心）
Prostate cancer is one of the most common causes of cancer deaths in men. There is a growing demand for noninvasively and accurately diagnostic methods that facilitate the current standard prostate cancer risk assessment in clinical practice. Still, developing computer-aided classification tools in prostate cancer diagnostics from multiparametric magnetic resonance images continues to be a challenge. In this work, we propose a novel deep learning approach for automatic classification of prostate lesions in the corresponding magnetic resonance images by constructing a two-stage multimodal multi…
Prostate cancer was the third most common cancer in 2020 internationally, coming after breast cancer and lung cancer. Furthermore, in recent years prostate cancer has shown an increasing trend. According to clinical experience, if this problem is detected and treated early, there can be a high chance of survival for the patient. One task that helps diagnose prostate cancer is prostate segmentation from magnetic resonance imaging. Manual segmentation performed by clinical experts has its drawbacks such as: the high time and concentration required from observers; and inter- and intra-observer va…
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…
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…
Mansoura University（曼苏拉大学）；Higher Institute of Engineering and Technology（高等工程技术学院）
Prostate cancer is the most dangerous cancer diagnosed in men worldwide. Prostate diagnosis has been affected by many factors, such as lesion complexity, observer visibility, and variability. Many techniques based on Magnetic Resonance Imaging (MRI) have been used for prostate cancer identification and classification in the last few decades. Developing these techniques is crucial and has a great medical effect because they improve the treatment benefits and the chance of patients' survival. A new technique that depends on MRI has been proposed to improve the diagnosis. This technique consists …
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…
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）
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…
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…
Location-based Radiology Report-Guided Semi-supervised Learning for Prostate Cancer Detection
National Cancer Institute（美国国家癌症研究所）；National Institutes of Health（美国国立卫生研究院）；Clinical Center, National Institutes of Health（美国国立卫生研究院临床中心）；Center for Cancer Research, National Cancer Institute（美国国家癌症研究所癌症研究中心）
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…
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,…
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 …
University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）；Sunnybrook Health Sciences Centre（桑尼布鲁克健康科学中心）
Prostate cancer is the most diagnosed form of cancer in Canadian men, and is the third leading cause of cancer death. Despite these statistics, prognosis is relatively good with a sufficiently early diagnosis, making fast and reliable prostate cancer detection crucial. As imaging-based prostate cancer screening, such as magnetic resonance imaging (MRI), requires an experienced medical professional to extensively review the data and perform a diagnosis, radiomics-driven methods help streamline the process and has the potential to significantly improve diagnostic accuracy and efficiency, and thu…
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…
Lunenfeld-Tanenbaum Research Institute（卢嫩菲尔德-塔南鲍姆研究所）；Sinai Health System（西奈卫生系统）；University of Toronto（多伦多大学）；Sunnybrook Research Institute（桑尼布鲁克研究所）
Prostate cancer is one of the most common forms of cancer and the third leading cause of cancer death in North America. As an integrated part of computer-aided detection (CAD) tools, diffusion-weighted magnetic resonance imaging (DWI) has been intensively studied for accurate detection of prostate cancer. With deep convolutional neural networks (CNNs) significant success in computer vision tasks such as object detection and segmentation, different CNNs architectures are increasingly investigated in medical imaging research community as promising solutions for designing more accurate CAD tools …
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…
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…
The diagnosis of prostate cancer faces a problem with overdiagnosis that leads to damaging side effects due to unnecessary treatment. Research has shown that the use of multi-parametric magnetic resonance images to conduct biopsies can drastically help to mitigate the overdiagnosis, thus reducing the side effects on healthy patients. This study aims to investigate the use of deep learning techniques to explore computer-aid diagnosis based on MRI as input. Several diagnosis problems ranging from classification of lesions as being clinically significant or not to the detection and segmentation o…
AI-based Automatic Segmentation of Prostate on Multi-modality Images: A Review
Soochow University（苏州大学）；Peking University First Hospital（北京大学第一医院）；Duke Kunshan University（昆山杜克大学）；Swinburne University of Technology（斯威本科技大学）；The Hong Kong Polytechnic University（香港理工大学）；UT Southwestern Medical Center（德克萨斯大学西南医学中心）
AI generated annotations for Breast, Brain, Liver, Lungs and Prostate cancer collections in National Cancer Institute Imaging Data Commons
BAMF Health（BAMF健康公司）；National Institute of Health（美国国立卫生研究院）；Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究实验室）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）
The MRI Institute for Biomedical Research（MRI 生物医学研究所）；Uber Technologies Inc.（优步技术公司）；LinkedIn（领英公司）；USAA（美国汽车协会联合服务银行）
A novel deep learning architecture (XmasNet) based on convolutional neural networks was developed for the classification of prostate cancer lesions, using the 3D multiparametric MRI data provided by the PROSTATEx challenge. End-to-end training was performed for XmasNet, with data augmentation done through 3D rotation and slicing, in order to incorporate the 3D information of the lesion. XmasNet outperformed traditional machine learning models based on engineered features, for both train and test data. For the test data, XmasNet outperformed 69 methods from 33 participating groups and achieved …
University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Universität Hamburg（汉堡大学）
Clinical decision support for histopathology image data mainly focuses on strongly supervised annotations, which offers intuitive interpretability, but is bound by expert performance. Here, we propose an explainable cancer relapse prediction network (eCaReNet) and show that end-to-end learning without strong annotations offers state-of-the-art performance while interpretability can be included through an attention mechanism. On the use case of prostate cancer survival prediction, using 14,479 images and only relapse times as annotations, we reach a cumulative dynamic AUC of 0.78 on a validatio…
HistoEncoder: a digital pathology foundation model for prostate cancer
University of Helsinki（赫尔辛基大学）；Helsinki University Hospital（赫尔辛基大学医院）；Skåne University Hospital（斯科讷大学医院）；Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki（赫尔辛基大学HiLIFE项目下属芬兰分子医学研究所（FIMM））；iCAN Digital Precision Cancer Medicine Flagship（iCAN数字精准癌症医学旗舰项目）
University of Electronic Science and Technology of China（电子科技大学）；University of Toronto（多伦多大学）；The Hospital for Sick Children (SickKids)（SickKids 儿童医院（病童医院））；Sinai Health System（西奈卫生系统）；University Health Network（大学健康网络）；Lunenfeld-Tanenbaum Research Institute（卢嫩菲尔德-塔南鲍姆研究所）；Sunnybrook Research Institute（桑尼布鲁克研究所）
Data augmentation refers to a group of techniques whose goal is to battle limited amount of available data to improve model generalization and push sample distribution toward the true distribution. While different augmentation strategies and their combinations have been investigated for various computer vision tasks in the context of deep learning, a specific work in the domain of medical imaging is rare and to the best of our knowledge, there has been no dedicated work on exploring the effects of various augmentation methods on the performance of deep learning models in prostate cancer detect…
University of Stavanger（斯塔万格大学）；Stavanger University Hospital（斯塔万格大学医院）；University of Agder（阿格德尔大学）
Prostate cancer (PCa) is the second most common cancer diagnosed among men worldwide. The current PCa diagnostic pathway comes at the cost of substantial overdiagnosis, leading to unnecessary treatment and further testing. Bi-parametric magnetic resonance imaging (bp-MRI) based on apparent diffusion coefficient maps (ADC) and T2-weighted (T2w) sequences has been proposed as a triage test to differentiate between clinically significant (cS) and non-clinically significant (ncS) prostate lesions. However, analysis of the sequences relies on expertise, requires specialized training, and suffers fr…
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）
PROFUSEme: PROstate Cancer Biochemical Recurrence Prediction via FUSEd Multi-modal Embeddings
Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA（计算病理学部，病理与实验室医学部，印第安纳大学医学院，印第安纳波利斯，印第安纳州，美国）；Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, USA（印第安纳大学Melvin和Bren Simon综合癌症中心，印第安纳波利斯，印第安纳州，美国）；Luddy School of Informatics, Computing, and Engineering, Indiana University, IN, USA（卢迪信息学、计算与工程学院，印第安纳大学，印第安纳州，美国）；Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA（放射学与成像科学部，印第安纳大学医学院，印第安纳波利斯，印第安纳州，美国）；Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA（生物统计学与健康数据科学部，印第安纳大学医学院，印第安纳波利斯，印第安纳州，美国）
German Cancer Research Center (DKFZ)（德国癌症研究中心）；University of Heidelberg Medical Center（海德堡大学医学中心）
Semantic segmentation constitutes an integral part of medical image analyses for which breakthroughs in the field of deep learning were of high relevance. The large number of trainable parameters of deep neural networks however renders them inherently data hungry, a characteristic that heavily challenges the medical imaging community. Though interestingly, with the de facto standard training of fully convolutional networks (FCNs) for semantic segmentation being agnostic towards the `structure' of the predicted label maps, valuable complementary information about the global quality of the segme…
Extracting and analyzing 3D histomorphometric features related to perineural and lymphovascular invasion in prostate cancer
Stanford University（斯坦福大学）；University of Washington（华盛顿大学）；University of Chicago（芝加哥大学）；University of California, Berkeley（加州大学伯克利分校）；Public Health Agency of Catalonia（加泰罗尼亚公共卫生局）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Atlanta VA Medical Center（亚特兰大退伍军人事务医疗中心）；University of Pennsylvania（宾夕法尼亚大学）
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）
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）
Interpretable Prostate Cancer Detection using a Small Cohort of MRI Images
Department of Computer Science, Boston University Metropolitan College（波士顿大学计算机科学系）；Department of Radiology, McMaster University（麦斯特大学放射科）；Department of Radiology, School of Medicine, 5th Azar Hospital, Golestan University of Medical Sciences（戈兰大学医学学院放射科，第五阿扎尔医院）；Department of Medical Imaging, Niagara Health System（尼亚加拉健康系统医学影像科）；Center of Excellence in Precision Medicine and Digital Health, Department of Physiology, Chulalongkorn University（朱拉隆梭大学精准医学与数字健康中心，生理学系）
Policy to Assist Iteratively Local Segmentation: Optimising Modality and Location Selection for Prostate Cancer Localisation
Department of Medical Physics and Biomedical Engineering, University College London, London, UK（医学物理与生物医学工程系，伦敦大学学院，伦敦，英国）；UCL Hawkes Institute, University College London, London, UK（UCL Hawkes研究所，伦敦大学学院，伦敦，英国）；School of Computer Science and Engineering, University of Westminster, London, UK（计算机科学与工程学院，伦敦威斯敏斯特大学，伦敦，英国）
Mask Enhanced Deeply Supervised Prostate Cancer Detection on B-mode Micro-Ultrasound
Stanford University（斯坦福大学）；The Catholic University of Korea（韩国天主教大学）；University of Alberta（阿尔伯塔大学）；University of California Los Angeles（加州大学洛杉矶分校）；Humanitas University（乌尼塔斯大学）；IRCCS Humanitas Research Hospital（IRCCS乌尼塔斯研究医院）；University of Florida（佛罗里达大学）；University of California San Diego（加州大学圣地亚哥分校）
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…
Classification of Prostate Cancer in 3D Magnetic Resonance Imaging Data based on Convolutional Neural Networks
Institut für Medizinische Informatik, Universität zu Lübeck（吕贝克大学医学信息学研究所）；FUSE-AI GmbH, Hamburg（汉堡FUSE-AI有限公司）；Cantonal Hospital Aarau, Aarau (CH)（阿劳州立医院）
East China University of Science and Technology（华东理工大学）
Prostate cancer is one of the common diseases in men, and it is the most common malignant tumor in developed countries. Studies have shown that the male prostate incidence rate is as high as 2.5% to 16%, Currently, the inci-dence of prostate cancer in Asia is lower than that in the West, but it is increas-ing rapidly. If prostate cancer can be found as early as possible and treated in time, it will have a high survival rate. Therefore, it is of great significance for the diagnosis and treatment of prostate cancer. In this paper, we propose a trans-fer learning method based on deep neural netwo…
Lunenfeld-Tanenbaum Research Institute（卢嫩菲尔德-塔南鲍姆研究所）；Sinai Health System（西奈卫生系统）；Brigham and Women’s Hospital（布莱根妇女医院）；University of Toronto（多伦多大学）
Convolutional Neural Networks (CNNs) have been used for automated detection of prostate cancer where Area Under Receiver Operating Characteristic (ROC) curve (AUC) is usually used as the performance metric. Given that AUC is not differentiable, common practice is to train the CNN using a loss functions based on other performance metrics such as cross entropy and monitoring AUC to select the best model. In this work, we propose to fine-tune a trained CNN for prostate cancer detection using a Genetic Algorithm to achieve a higher AUC. Our dataset contained 6-channel Diffusion-Weighted MRI slices…
DiagSet: a dataset for prostate cancer histopathological image classification
Diagnostyka Consilio Sp. z o.o.（诊断康西利奥有限公司）；AGH University of Science and Technology（AGH科技大学）；Andrzej Frycz Modrzewski Krakow University（安德烈·弗赖奇·莫德热夫斯基克拉科夫大学）
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…
