Automated Scoring of Nuclear Pleomorphism Spectrum with Pathologist-level Performance in Breast Cancer
Radboud University Medical Center（拉德堡德大学医学中心）；GZA-ZNA Hospitals（GZA-ZNA医院）；Peter Mac Callum Cancer Centre（彼得·麦卡勒姆癌症中心）；Mayo Clinic（梅奥诊所）；Medipath & American Hospital of Paris（Medipath与巴黎美国医院）；Canisius Wilhelmina Ziekenhuis（卡尼修斯·威廉米娜医院）；University of Porto（波尔图大学）；The Netherlands Cancer Institute（荷兰癌症研究所）；Technical University Munich（慕尼黑工业大学）；Hannover Medical School（汉诺威医学院）
Caner Mercan, Maschenka Balkenhol, Roberto Salgado, Mark Sherman, Philippe Vielh, Willem Vreuls, Antonio Polonia, Hugo M. Horlings, Wilko Weichert, Jodi M. Cart… 展开作者
Caner Mercan, Maschenka Balkenhol, Roberto Salgado, Mark Sherman, Philippe Vielh, Willem Vreuls, Antonio Polonia, Hugo M. Horlings, Wilko Weichert, Jodi M. Carter, Peter Bult, Matthias Christgen, Carsten Denkert, Koen van de Vijver, Jeroen van der Laak, Francesco Ciompi
Nuclear pleomorphism, defined herein as the extent of abnormalities in the overall appearance of tumor nuclei, is one of the components of the three-tiered breast cancer grading. Given that nuclear pleomorphism reflects a continuous spectrum of variation, we trained a deep neural network on a large variety of tumor regions from the collective knowledge of several pathologists, without constraining the network to the traditional three-category classification. We also motivate an additional approach in which we discuss the additional benefit of normal epithelium as baseline, following the routine clinical practice where pathologists are trained to score nuclear pleomorphism in tumor, having the normal breast epithelium for comparison. In multiple experiments, our fully-automated approach could achieve top pathologist-level performance in select regions of interest as well as at whole slide images, compared to ten and four pathologists, respectively.
Breast cancer is one of the most common cancer in women around the world. For diagnosis, pathologists evaluate biomarkers such as HER2 protein using immunohistochemistry over tissue extracted by a biopsy. Through microscopic inspection, this assessment estimates the intensity and integrity of the membrane cells' staining and scores the sample as 0, 1+, 2+, or 3+: a subjective decision that depends on the interpretation of the pathologist. This paper presents the preliminary data analysis of the annotations of three pathologists over the same set of samples obtained using 20x magnification and …
Neuromation OU；University of Oulu（奥卢大学）；MIT（麻省理工学院）；University of Michigan（密歇根大学）；Steklov Mathematical Institute（斯捷克洛夫数学研究所）
Breast cancer is one of the main causes of death worldwide. Histopathological cellularity assessment of residual tumors in post-surgical tissues is used to analyze a tumor's response to a therapy. Correct cellularity assessment increases the chances of getting an appropriate treatment and facilitates the patient's survival. In current clinical practice, tumor cellularity is manually estimated by pathologists; this process is tedious and prone to errors or low agreement rates between assessors. In this work, we evaluated three strong novel Deep Learning-based approaches for automatic assessment…
Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；Weill Cornell Graduate School for Medical Sciences（威尔康奈尔医学研究生院）
Pathologic analysis of surgical excision specimens for breast carcinoma is important to evaluate the completeness of surgical excision and has implications for future treatment. This analysis is performed manually by pathologists reviewing histologic slides prepared from formalin-fixed tissue. In this paper, we present Deep Multi-Magnification Network trained by partial annotation for automated multi-class tissue segmentation by a set of patches from multiple magnifications in digitized whole slide images. Our proposed architecture with multi-encoder, multi-decoder, and multi-concatenation out…
Indian Institute of Information Technology Allahabad（阿拉哈巴德印度信息技术学院）
In the clinical settings, during digital examination of histopathological slides, the pathologist annotate the slides by marking the rough boundary around the suspected tumour region. The marking or annotation is generally represented as a polygonal boundary that covers the extent of the tumour in the slide. These polygonal markings are difficult to imitate through CAD techniques since the tumour regions are heterogeneous and hence segmenting them would require exhaustive pixel wise ground truth annotation. Therefore, for CAD analysis, the ground truths are generally annotated by pathologist e…
Cancer is one of the most common and fatal diseases in the world. Breast cancer affects one in every eight women and one in every eight hundred men. Hence, our prime target should be early detection of cancer because the early detection of cancer can be helpful to cure cancer effectively. Therefore, we propose a saliency detection system with the help of advanced deep learning techniques, such that the machine will be taught to emulate actions of pathologists for localization of diagnostically pertinent regions. We study identification of five diagnostic categories of breast cancer by training…
National Chiao Tung University（国立交通大学）；Liouying Chi Mei Hospital（柳营奇美医院）；National Pingtung University of Science and Technology（国立屏东科技大学）
Ki-67 is a nuclear protein that can be produced during cell proliferation. The Ki67 index is a valuable prognostic variable in several kinds of cancer. In breast cancer, the index is even routinely checked in many patients. Currently, pathologists use the immunohistochemistry method to calculate the percentage of Ki-67 positive malignant cells as Ki-67 index. The higher score usually means more aggressive tumor behavior. In clinical practice, the measurement of Ki-67 index relies on visual identifying method and manual counting. However, visual and manual assessment method is timeconsuming and…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Xiangya Hospital, Central South University（中南大学湘雅医院）；University of Chinese Academy of Sciences（中国科学院大学）；Central South University（中南大学）
Molecular subtypes of breast cancer are important references to personalized clinical treatment. For cost and labor savings, only one of the patient's paraffin blocks is usually selected for subsequent immunohistochemistry (IHC) to obtain molecular subtypes. Inevitable sampling error is risky due to tumor heterogeneity and could result in a delay in treatment. Molecular subtype prediction from conventional H&E pathological whole slide images (WSI) using AI method is useful and critical to assist pathologists pre-screen proper paraffin block for IHC. It's a challenging task since only WSI level…
ECTIL: Label-efficient Computational Tumour Infiltrating Lymphocyte (TIL) assessment in breast cancer: Multicentre validation in 2,340 patients with breast cancer
University of Amsterdam（阿姆斯特丹大学）；Netherlands Cancer Institute（荷兰癌症研究所）；Erasmus University Medical Center（伊拉斯姆斯大学医学中心）；University Medical Center Groningen（格罗宁根大学医学中心）；The Amsterdam University Medical Centre, AUMC, The University of Amsterdam（阿姆斯特丹大学医学中心（阿姆斯特丹大学））；ZAS Hospitals（ZAS医院）；Peter Mac Callum Centre（彼得·麦卡勒姆中心）；University Medical Center Utrecht（乌得勒支大学医学中心）
Umm Al-Qura University（乌姆·库拉大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；University of Tabuk（塔布克大学）；King Faisal Specialist Hospital and Research Center（费萨尔国王专科医院和研究中心）；University of Nevada（内华达大学）
Breast cancer is the most common cancer in the world and the second most common type of cancer that causes death in women. The timely and accurate diagnosis of breast cancer using histopathological images is crucial for patient care and treatment. Pathologists can make more accurate diagnoses with the help of a novel approach based on image processing. This approach is an ensemble model of two types of pre-trained vision transformer models, namely, Vision Transformer and Data-Efficient Image Transformer. The proposed ensemble model classifies breast cancer histopathology images into eight clas…
Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides
Norwegian University of Science and Technology (NTNU)（挪威科技大学）；St. Olavs hospital, Trondheim University Hospital（圣奥拉夫医院，特隆赫姆大学医院）；SINTEF Digital（SINTEF数字研究所）；Sopra Steria（索普瑞斯特利亚）；Uppsala University（乌普萨拉大学）；University of Bergen（卑尔根大学）；Haukeland University Hospital（豪克兰大学医院）
FECT: Classification of Breast Cancer Pathological Images Based on Fusion Features
Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School（清华深圳国际研究生院生物制药与健康工程研究所）；School of Medicine, Nankai University（南开大学医学院）
GRAPHITE: Graph-Based Interpretable Tissue Examination for Enhanced Explainability in Breast Cancer Histopathology
School of Computer Science and Engineering（计算机科学与工程学院）；University of New South Wales（新南威尔士大学）；St. George and Sutherland Clinical School（圣乔治与萨瑟兰临床学院）；Department of Anatomical Pathology（解剖病理学部）；NSW Health Pathology（新南威尔士州卫生病理学）；Cancer Care Centre（癌症护理中心）
University of Warwick（华威大学）；Queen Mary University of London（伦敦玛丽女王大学）；University of Sheffield（谢菲尔德大学）
The quantification of tumor-infiltrating lymphocytes (TILs) has been shown to be an independent predictor for prognosis of breast cancer patients. Typically, pathologists give an estimate of the proportion of the stromal region that contains TILs to obtain a TILs score. The Tumor InfiltratinG lymphocytes in breast cancER (TiGER) challenge, aims to assess the prognostic significance of computer-generated TILs scores for predicting survival as part of a Cox proportional hazards model. For this challenge, as the TIAger team, we have developed an algorithm to first segment tumor vs. stroma, before…
Institute of Pathology, University Hospital Carl Gustav Carus (UKD), TU Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯大学医院病理研究所）；ASGEN GmbH & Co. KG（ASGEN两合有限责任公司）；National Center for Tumor Diseases (NCT), Partner Site Dresden（国家肿瘤疾病中心（德累斯顿合作站点））；ContextVision AB（康泰视伦公司）；Institute for Medical Informatics and Biometry (IMB), Carl Gustav Carus Faculty of Medicine, TU Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯医学院医学信息学与生物测量学研究所）；Institute of Bioengineering, School of Life Sciences, EPFL（洛桑联邦理工学院生命科学学院生物工程研究所）；Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG)（马克斯·普朗克分子细胞生物学与遗传学研究所）；Center for Systems Biology Dresden (CSBD)（德累斯顿系统生物学中心）
Histo-pathological diagnostics are an inherent part of the everyday work but are particularly laborious and associated with time-consuming manual analysis of image data. In order to cope with the increasing diagnostic case numbers due to the current growth and demographic change of the global population and the progress in personalized medicine, pathologists ask for assistance. Profiting from digital pathology and the use of artificial intelligence, individual solutions can be offered (e.g. detect labeled cancer tissue sections). The testing of the human epidermal growth factor receptor 2 (HER…
Universidad Técnica Particular de Loja（洛哈技术大学）；Universitat Politécnica de Valencia（瓦伦西亚理工大学）；University of Waterloo（滑铁卢大学）
Breast cancer is the most common invasive cancer in women, and the second main cause of death. Breast cancer screening is an efficient method to detect indeterminate breast lesions early. The common approaches of screening for women are tomosynthesis and mammography images. However, the traditional manual diagnosis requires an intense workload by pathologists, who are prone to diagnostic errors. Thus, the aim of this study is to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images. Based on deep learn…
National Cancer Institute – IRCCS – Fondazione Pascale（国家癌症研究所——IRCCS——帕斯卡尔基金会）；IBM Research – Zurich（IBM研究院——苏黎世）；ETH Zurich（苏黎世联邦理工学院）；Institute for High Performance Computing and Networking of the Research Council of Italy, ICAR-CNR（意大利研究理事会高性能计算与网络研究所，ICAR-CNR）；University of Naples Federico II（那不勒斯费德里科二世大学）；EPFL Lausanne（洛桑联邦理工学院）
Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women with cancer. Recent advancements in diagnostic activities combined with large-scale screening policies have significantly lowered the mortality rates for breast cancer patients. However, the manual inspection of tissue slides by the pathologists is cumbersome, time-consuming, and is subject to significant inter- and intra-observer variability. Recently, the advent of whole-slide scanning systems have empowered the rapid digitization of pathology slides, and enabled to develop digital workfl…
FEMTO-ST institute（弗朗什-孔泰电子、机械、热学与光学科学与技术研究所）；Univ. Bourgogne Franche-Comte（勃艮第-弗朗什-孔泰大学）；CNRS（法国国家科学研究中心）；ENSMM（国立高等机械与微技术学院）；Beihang University（北京航空航天大学）；Hôpital Nord Franche-Comté（北弗朗什-孔泰医院）；Batna2 University（巴特纳第二大学）
The Ki-67 proliferation index is an essential biomarker that helps pathologists to diagnose and select appropriate treatments. However, automatic evaluation of Ki-67 is difficult due to nuclei overlapping and complex variations in their properties. This paper proposes an integrated pipeline for accurate automatic counting of Ki-67, where the impact of nuclei separation techniques is highlighted. First, semantic segmentation is performed by combining the Squeez and Excitation Resnet and Unet algorithms to extract nuclei from the background. The extracted nuclei are then divided into overlapped …
The overexpression of human epidermal growth factor receptor 2 (HER2) has been established as a therapeutic target in multiple types of cancers, such as breast and gastric cancers. Immunohistochemistry (IHC) is employed as a basic HER2 test to identify the HER2-positive, borderline, and HER2-negative patients. However, the reliability and accuracy of HER2 scoring are affected by many factors, such as pathologists' experience. Recently, artificial intelligence (AI) has been used in various disease diagnosis to improve diagnostic accuracy and reliability, but the interpretation of diagnosis resu…
Breast cancer is the most common cancer among women worldwide. The human epidermal growth factor receptor 2 (HER2) with immunohistochemical (IHC) is widely used for pathological evaluation to provide the appropriate therapy for patients with breast cancer. However, the deficiency of pathologists and subjective and susceptible to inter-observer variation of visual diagnosis are the main challenges. Recently, with the rapid development of artificial intelligence (AI) in disease diagnosis, several automated HER2 scoring methods using traditional computer vision or machine learning methods indicat…
Breast cancer is the most commonly diagnosed cancer worldwide, with over two million new cases each year. During diagnostic tumour grading, pathologists manually count the number of dividing cells (mitotic figures) in biopsy or tumour resection specimens. Since the process is subjective and time-consuming, data-driven artificial intelligence (AI) methods have been developed to automatically detect mitotic figures. However, these methods often generalise poorly, with performance reduced by variations in tissue types, staining protocols, or the scanners used to digitise whole-slide images. Domai…
University of Toronto（多伦多大学）；National Cancer Research Centre（国家癌症研究中心）；Institute for Oncology and Radiology（肿瘤与放射学研究所）
Accurate diagnosis of breast cancer in histopathology images is challenging due to the heterogeneity of cancer cell growth as well as of a variety of benign breast tissue proliferative lesions. In this paper, we propose a practical and self-interpretable invasive cancer diagnosis solution. With minimum annotation information, the proposed method mines contrast patterns between normal and malignant images in unsupervised manner and generates a probability map of abnormalities to verify its reasoning. Particularly, a fully convolutional autoencoder is used to learn the dominant structural patter…
The evaluation of the Human Epidermal growth factor Receptor-2 (HER2) expression is an important prognostic biomarker for breast cancer treatment selection. However, HER2 scoring has notoriously high interobserver variability due to stain variations between centers and the need to estimate visually the staining intensity in specific percentages of tumor area. In this paper, focusing on the interpretability of HER2 scoring by a pathologist, we propose a semi-automatic, two-stage deep learning approach that directly evaluates the clinical HER2 guidelines defined by the American Society of Clinic…
University of Washington（华盛顿大学）；University of Vermont College of Medicine（佛蒙特大学医学院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
Training end-to-end networks for classifying gigapixel size histopathological images is computationally intractable. Most approaches are patch-based and first learn local representations (patch-wise) before combining these local representations to produce image-level decisions. However, dividing large tissue structures into patches limits the context available to these networks, which may reduce their ability to learn representations from clinically relevant structures. In this paper, we introduce a novel attention-based network, the Holistic ATtention Network (HATNet) to classify breast biops…
Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院肿瘤医院（国家癌症中心/国家临床肿瘤医学中心）深圳医院）；Research Institute of Tsinghua, Guangzhou（清华大学广州研究院）
Efficient Breast and Ovarian Cancer Classification via ViT-Based Preprocessing and Transfer Learning
Department of Mathematics, University of Texas at Arlington（德克萨斯大学阿灵顿分校数学系）；Department of Data Science and Mathematics, Embry-Riddle Aeronautical University（埃姆伯里-瑞德航空大学数据科学与数学系）
Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries
University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Meta Platforms Inc.（元平台公司）
A Preliminary Investigation into Search and Matching for Tumour Discrimination in WHO Breast Taxonomy Using Deep Networks
Mayo Clinic（梅奥诊所）；University of Waterloo（滑铁卢大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）；Luma Medical Centre（卢玛医疗中心）；International Agency for Research on Cancer（国际癌症研究机构）；Rhazes Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA（梅奥诊所Rhazes实验室（人工智能与信息学系））；Kimia Lab, University of Waterloo, Canada（滑铁卢大学Kimia实验室）
Beijing Chao-Yang Hospital（北京朝阳医院）；Beijing University of Posts and Telecommunications（北京邮电大学）
Objectives: To develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN. Methods: A total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) …
Rose-Hulman Institute of Technology（罗斯-霍曼理工学院）；Honeywell FM&T（霍尼韦尔FM&T）
Classification of cancer cellularity within tissue samples is currently a manual process performed by pathologists. This process of correctly determining cancer cellularity can be time intensive. Deep Learning (DL) techniques in particular have become increasingly more popular for this purpose, due to the accuracy and performance they exhibit, which can be comparable to the pathologists. This work investigates the capabilities of two DL approaches to assess cancer cellularity in whole slide images (WSI) in the SPIE-AAPM-NCI BreastPathQ challenge dataset. The effects of training on augmented da…
Automated Volume Corrected Mitotic Index Calculation Through Annotation-Free Deep Learning using Immunohistochemistry as Reference Standard
Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）；The Schwarzman Animal Medical Center（施瓦茨曼动物医疗中心）；Institute of Veterinary Pathology, Freie Universität Berlin（柏林自由大学兽医学病理学研究所）；Institute of Pathology, University of Veterinary Medicine Vienna（维也纳兽医大学病理学研究所）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）
Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling
University of California, Los Angeles（加州大学洛杉矶分校）；California NanoSystems Institute（加州纳米系统研究所）；University of California at Davis（加州大学戴维斯分校）；Bnai-Zion Medical Center（贝内锡安医疗中心）；Hadassah Hebrew University Medical Center（哈达萨希伯来大学医疗中心）；David Geffen School of Medicine, University of California, Los Angeles（加州大学洛杉矶分校大卫·格芬医学院）
Cancer is the uncontrollable cell division of abnormal cells inside the human body, which can spread to other body organs. It is one of the non-communicable diseases (NCDs) and NCDs accounts for 71% of total deaths worldwide whereas lung cancer is the second most diagnosed cancer after female breast cancer. Cancer survival rate of lung cancer is only 19%. There are various methods for the diagnosis of lung cancer, such as X-ray, CT scan, PET-CT scan, bronchoscopy and biopsy. However, to know the subtype of lung cancer based on the tissue type H and E staining is widely used, where the staining…
Primaa；Rennes University hospital（雷恩大学医院）；Paris Saint-Joseph hospital（巴黎圣约瑟夫医院）；Brest university hospital（布雷斯特大学医院）；Saint-Antoine hospital, APHP（圣安托万医院，APHP）；Henri-Mondor hospital, APHP（亨利-蒙多尔医院，APHP）；René Dubos hospital（勒内·杜博斯医院）；Bicêtre hospital, APHP, Paris Saclay University（比塞特医院，APHP，巴黎萨克雷大学）；Nice University hospital（尼斯大学医院）
Breast cancer is one of the most prevalent cancers worldwide and pathologists are closely involved in establishing a diagnosis. Tools to assist in making a diagnosis are required to manage the increasing workload. In this context, artificial intelligence (AI) and deep-learning based tools may be used in daily pathology practice. However, it is challenging to develop fast and reliable algorithms that can be trusted by practitioners, whatever the medical center. We describe a patch-based algorithm that incorporates a convolutional neural network to detect and locate invasive carcinoma on breast …
Netherlands Cancer Institute（荷兰癌症研究所）；University of Amsterdam（阿姆斯特丹大学）；VU University Amsterdam（阿姆斯特丹自由大学）；Ellogon AI B.V.（Ellogon AI 公司）；Radboud University Medical Center（拉德堡德大学医学中心）
We present WeakSTIL, an interpretable two-stage weak label deep learning pipeline for scoring the percentage of stromal tumor infiltrating lymphocytes (sTIL%) in H&E-stained whole-slide images (WSIs) of breast cancer tissue. The sTIL% score is a prognostic and predictive biomarker for many solid tumor types. However, due to the high labeling efforts and high intra- and interobserver variability within and between expert annotators, this biomarker is currently not used in routine clinical decision making. WeakSTIL compresses tiles of a WSI using a feature extractor pre-trained with self-supervi…
Recently, large, high-quality public datasets have led to the development of convolutional neural networks that can detect lymph node metastases of breast cancer at the level of expert pathologists. Many cancers, regardless of the site of origin, can metastasize to lymph nodes. However, collecting and annotating high-volume, high-quality datasets for every cancer type is challenging. In this paper we investigate how to leverage existing high-quality datasets most efficiently in multi-task settings for closely related tasks. Specifically, we will explore different training and domain adaptation…
Karolinska Institutet（卡罗林斯卡学院）；Karolinska University Hospital（卡罗林斯卡大学医院）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所）；MedTechLabs
Computational pathology methods have the potential to improve access to precision medicine, as well as the reproducibility and accuracy of pathological diagnoses. Particularly the analysis of whole-slide-images (WSIs) of immunohistochemically (IHC) stained tissue sections could benefit from computational pathology methods. However, scoring biomarkers such as KI67 in IHC WSIs often necessitates the detection of areas of invasive cancer. Training cancer detection models often requires annotations, which is time-consuming and therefore costly. Currently, cancer regions are typically annotated in …
Digitization of histopathology slides has led to several advances, from easy data sharing and collaborations to the development of digital diagnostic tools. Deep learning (DL) methods for classification and detection have shown great potential, but often require large amounts of training data that are hard to collect, and annotate. For many cancer types, the scarceness of data creates barriers for training DL models. One such scenario relates to detecting tumor metastasis in lymph node tissue, where the low ratio of tumor to non-tumor cells makes the diagnostic task hard and time-consuming. DL…
Zonguldak Bulent Ecevit University（宗古尔达克·比伦特·埃杰维特大学）
Cancer disease is one of the leading causes of death all over the world. Breast cancer, which is a common cancer disease especially in women, is quite common. The most important tool used for early detection of this cancer type, which requires a long process to establish a definitive diagnosis, is histopathological images taken by biopsy. These obtained images are examined by pathologists and a definitive diagnosis is made. It is quite common to detect this process with the help of a computer. Detection of benign or malignant tumors, especially by using data with different magnification rates,…
Histopathological images of tumors contain abundant information about how tumors grow and how they interact with their micro-environment. Better understanding of tissue phenotypes in these images could reveal novel determinants of pathological processes underlying cancer, and in turn improve diagnosis and treatment options. Advances of Deep learning makes it ideal to achieve those goals, however, its application is limited by the cost of high quality labels from patients data. Unsupervised learning, in particular, deep generative models with representation learning properties provides an alter…
