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）
Michał Koziarski, Bogusław Cyganek, Przemysław Niedziela, Bogusław Olborski, Zbigniew Antosz, Marcin Żydak, Bogdan Kwolek, Paweł Wąsowicz, Andrzej Bukała, Jakub… 展开作者
Michał Koziarski, Bogusław Cyganek, Przemysław Niedziela, Bogusław Olborski, Zbigniew Antosz, Marcin Żydak, Bogdan Kwolek, Paweł Wąsowicz, Andrzej Bukała, Jakub Swadźba, Piotr Sitkowski
Cancer diseases constitute one of the most significant societal challenges. In this paper, we introduce a novel histopathological dataset for prostate cancer detection. The proposed dataset, consisting of over 2.6 million tissue patches extracted from 430 fully annotated scans, 4675 scans with assigned binary diagnoses, and 46 scans with diagnoses independently provided by a group of histopathologists can be found at https://github.com/michalkoziarski/DiagSet. Furthermore, we propose a machine learning framework for detection of cancerous tissue regions and prediction of scan-level diagnosis, utilizing thresholding to abstain from the decision in uncertain cases. The proposed approach, composed of ensembles of deep neural networks operating on the histopathological scans at different scales, achieves 94.6% accuracy in patch-level recognition and is compared in a scan-level diagnosis with 9 human histopathologists showing high statistical agreement.
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…
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…
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 …
Histology method is vital in the diagnosis and prognosis of cancers and many other diseases. For the analysis of histopathological images, we need to detect and segment all gland structures. These images are very challenging, and the task of segmentation is even challenging for specialists. Segmentation of glands determines the grade of cancer such as colon, breast, and prostate. Given that deep neural networks have achieved high performance in medical images, we propose a method based on the LinkNet network for gland segmentation. We found the effects of using different loss functions. By usi…
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…
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）
Deep learning-based analysis of high-frequency, high-resolution micro-ultrasound data shows great promise for prostate cancer detection. Previous approaches to analysis of ultrasound data largely follow a supervised learning paradigm. Ground truth labels for ultrasound images used for training deep networks often include coarse annotations generated from the histopathological analysis of tissue samples obtained via biopsy. This creates inherent limitations on the availability and quality of labeled data, posing major challenges to the success of supervised learning methods. On the other hand, …
Radboud University Medical Center（拉德堡德大学医学中心）；Linköping University（林雪平大学）
Prostate cancer is the most prevalent cancer among men in Western countries, with 1.1 million new diagnoses every year. The gold standard for the diagnosis of prostate cancer is a pathologists' evaluation of prostate tissue. To potentially assist pathologists deep-learning-based cancer detection systems have been developed. Many of the state-of-the-art models are patch-based convolutional neural networks, as the use of entire scanned slides is hampered by memory limitations on accelerator cards. Patch-based systems typically require detailed, pixel-level annotations for effective training. How…
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）
Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. To shed light on this problem, we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease. First, we find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that differentiates critical structures of thin tissue sections with subcellular spatial resolution, including nuclei, cytoplasm, stroma, basal cells, nerves…
Generalisation of automatic tumour segmentation in histopathological whole-slide images across multiple cancer types
Institute for Cancer Genetics and Informatics（癌症基因组学与信息学研究所）；Oslo University Hospital（奥斯陆大学医院）；University of Oslo（奥斯陆大学）；Department of Gastrointestinal（胃肠科部）；Institute of Clinical Medicine（临床医学研究所）；Department of Gynaecological Oncology（妇科肿瘤科部）；Amsterdam University Medical Centres（阿姆斯特丹大学医学中心）；Department of Obstetrics and Gynaecology（妇产科部）；Comprehensive Cancer Center Innsbruck（因斯布鲁克综合癌症中心）；Innsbruck Medical University（因斯布鲁克医学院）；Vestre Viken Hospital Trust（维斯特维肯医院信托）；Department of Medical Biology（医学生物学部）；UiT The Arctic University of Norway（挪威北极大学（UiT））；Department of Clinical Pathology（临床病理学部）
Massachusetts Institute of Technology（麻省理工学院）；Harvard Medical School（哈佛医学院）；Boston University School of Medicine（波士顿大学医学院）；VA Boston Healthcare（波士顿退伍军人医疗中心）
Histopathological diagnoses of tumors in tissue biopsy after Hematoxylin and Eosin (H&E) staining is the gold standard for oncology care. H&E staining is slow and uses dyes, reagents and precious tissue samples that cannot be reused. Thousands of native nonstained RGB Whole Slide Image (RWSI) patches of prostate core tissue biopsies were registered with their H&E stained versions. Conditional Generative Adversarial Neural Networks (cGANs) that automate conversion of native nonstained RWSI to computational H&E stained images were then trained. High similarities between computational and H&E dye…
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,…
Pancreatic cancer will soon be the second leading cause of cancer-related death in Western society. Imaging techniques such as CT, MRI and ultrasound typically help providing the initial diagnosis, but histopathological assessment is still the gold standard for final confirmation of disease presence and prognosis. In recent years machine learning approaches and pathomics pipelines have shown potential in improving diagnostics and prognostics in other cancerous entities, such as breast and prostate cancer. A crucial first step in these pipelines is typically identification and segmentation of t…
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）
Towards More Transparent and Accurate Cancer Diagnosis with an Unsupervised CAE Approach
Tyris Tech S.L.（泰瑞斯科技公司）；Universitat Politècnica de València（瓦伦西亚理工大学）；ValgrAI – Valencian Graduate School and Research Network for Artificial Intelligence（瓦伦西亚人工智能研究生院与研究网络）
