Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical practice. The two key challenges in developing automated methods for DBT classification are handling the variable number of slices and retaining slice-to-slice changes. We propose a novel deep 2D convolutional neural network (CNN) architecture for DBT classification that simultaneously overcomes both challenges. Our approach operates on the full volume, regardless of the number of slices, and allows the use of pre-trained 2D CNNs for feature extraction, which is important given the limited amount of annotated training data. In an extensive evaluation on a real-world clinical dataset, our approach achieves 0.854 auROC, which is 28.80% higher than approaches based on 3D CNNs. We also find that these improvements are stable across a range of model configurations.
DeepHealth, Inc.（深健公司）；Rhode Island Hospital（罗德岛医院）；Brown University（布朗大学）；Henan Provincial People’s Hospital（河南省人民医院）；Medford Radiology Group（梅德福放射学集团）；University of Massachusetts Medical School（马萨诸塞大学医学院）
Breast cancer remains a global challenge, causing over 1 million deaths globally in 2018. To achieve earlier breast cancer detection, screening x-ray mammography is recommended by health organizations worldwide and has been estimated to decrease breast cancer mortality by 20-40%. Nevertheless, significant false positive and false negative rates, as well as high interpretation costs, leave opportunities for improving quality and access. To address these limitations, there has been much recent interest in applying deep learning to mammography; however, obtaining large amounts of annotated data p…
Breast cancer screening is one of the most common radiological tasks with over 39 million exams performed each year. While breast cancer screening has been one of the most studied medical imaging applications of artificial intelligence, the development and evaluation of the algorithms are hindered due to the lack of well-annotated large-scale publicly available datasets. This is particularly an issue for digital breast tomosynthesis (DBT) which is a relatively new breast cancer screening modality. We have curated and made publicly available a large-scale dataset of digital breast tomosynthesis…
Towards Early Detection: AI-Based Five-Year Forecasting of Breast Cancer Risk Using Digital Breast Tomosynthesis Imaging
Athinoula A. Martinos Center for Biomedical Imaging, Mass General Brigham and Harvard Medical School（Athinoula A. Martinos 生物医学成像中心，马萨诸塞总医院与哈佛医学院）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin（柏林夏里特医学院，自由大学和洪堡大学的成员）；Department of Radiology, University of Wisconsin School of Medicine and Public Health（威斯康星大学医学与公共卫生学院放射科）
A novel hierarchical model is introduced to solve a general problem of detecting groups of similar objects. Under this model, detection of groups is performed in hierarchically organized layers while each layer represents a scope for target objects. The processing of these layers involves sequential extraction of appearance features for an individual object, consistency measurement features for nearby objects, and finally the distribution features for all objects within the group. Using the concept of scope-based normalization, the extracted features not only enhance local contrast of an indiv…
Radboud University Medical Center（拉德堡德大学医学中心）；Delft University of Technology（代尔夫特理工大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Elekta（医科达）；Dutch Expert Centre for Screening（荷兰筛查专家中心）
Digital breast tomosynthesis is rapidly replacing digital mammography as the basic x-ray technique for evaluation of the breasts. However, the sparse sampling and limited angular range gives rise to different artifacts, which manufacturers try to solve in several ways. In this study we propose an extension of the Learned Primal-Dual algorithm for digital breast tomosynthesis. The Learned Primal-Dual algorithm is a deep neural network consisting of several `reconstruction blocks', which take in raw sinogram data as the initial input, perform a forward and a backward pass by taking projections a…
Mammography-based screening has helped reduce the breast cancer mortality rate, but has also been associated with potential harms due to low specificity, leading to unnecessary exams or procedures, and low sensitivity. Digital breast tomosynthesis (DBT) improves on conventional mammography by increasing both sensitivity and specificity and is becoming common in clinical settings. However, deep learning (DL) models have been developed mainly on conventional 2D full-field digital mammography (FFDM) or scanned film images. Due to a lack of large annotated DBT datasets, it is difficult to train a …
University of São Paulo（圣保罗大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Fudan University（复旦大学）；Shanghai Center for Brain Science and Brain-inspired Technology（上海脑科学与类脑研究中心）
Digital breast tomosynthesis (DBT) exams should utilize the lowest possible radiation dose while maintaining sufficiently good image quality for accurate medical diagnosis. In this work, we propose a convolution neural network (CNN) to restore low-dose (LD) DBT projections to achieve an image quality equivalent to a standard full-dose (FD) acquisition. The proposed network architecture benefits from priors in terms of layers that were inspired by traditional model-based (MB) restoration methods, considering a model-based deep learning approach, where the network is trained to operate in the va…
Digital Breast Tomosynthesis (DBT) provides an insight into the fine details of normal fibroglandular tissues and abnormal lesions by reconstructing a pseudo-3D image of the breast. In this respect, DBT overcomes a major limitation of conventional X-ray mammography by reducing the confounding effects caused by the superposition of breast tissue. In a breast cancer screening or diagnostic context, a radiologist is interested in detecting change, which might be indicative of malignant disease. To help automate this task image registration is required to establish spatial correspondence between t…
A Workflow to Efficiently Generate Dense Tissue Ground Truth Masks for Digital Breast Tomosynthesis
Department of Radiology, University of Pittsburgh（匹兹堡大学放射科）；Center of Informatics, Federal University of Paraíba（巴西北部联邦大学信息中心）；Department of Radiology, University of Pennsylvania（宾夕法尼亚大学放射科）；Department of Industrial Engineering, University of Pittsburgh（匹兹堡大学工业工程系）
University of Kentucky（肯塔基大学）；Miami University（迈阿密大学）
Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D mammogram) are the two types of mammography imagery that are used in clinical practice for breast cancer detection and diagnosis. Radiologists usually read both imaging modalities in combination; however, existing computer-aided diagnosis tools are designed using only one imaging modality. Inspired by clinical practice, we propose an innovative convolutional neural network (CNN) architecture for breast cancer classi…
DBT-DINO: Towards Foundation model based analysis of Digital Breast Tomosynthesis
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School（阿提诺拉·A·马丁诺生物医学影像中心、麻省总医院和哈佛医学院）；Department of Radiology, Charité - Universitätsmedizin Berlin（放射科、柏林夏里特大学医学院）；Mass General Brigham Data Science Office（麻省总医院数据科学办公室）；Department of Computer Science, Institute for Machine Learning, ETH Zürich（计算机科学系、机器学习研究所、苏黎世联邦理工学院）；Massachusetts General Hospital Cancer Center and Harvard Medical School（麻省总医院癌症中心和哈佛医学院）；Department of Radiology, Massachusetts General Hospital（放射科、麻省总医院）；Department of Radiology, University of Wisconsin School of Medicine and Public Health（放射科、威斯康星大学医学与公共卫生学院）
Tsinghua University（清华大学）；Z 2 Sky Technologies Inc.（Z2天空科技公司）；Affiliated Hospital of Guizhou Medical University（贵州医科大学附属医院）；Naval Medical University（海军军医大学）；Changzheng Hospital（长征医院）；University of Science and Technology of China（中国科学技术大学）
Digital chest tomosynthesis (DCT) is a technique to produce sectional 3D images of a human chest for pulmonary disease screening, with 2D X-ray projections taken within an extremely limited range of angles. However, under the limited angle scenario, DCT contains strong artifacts caused by the presence of ribs, jamming the imaging quality of the lung area. Recently, great progress has been achieved for rib suppression in a single X-ray image, to reveal a clearer lung texture. We firstly extend the rib suppression problem to the 3D case at the software level. We propose a $\textbf{T}$omosynthesi…
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 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…
Purpose: To develop and evaluate the accuracy of a multi-view deep learning approach to the analysis of high-resolution synthetic mammograms from digital breast tomosynthesis screening cases, and to assess the effect on accuracy of image resolution and training set size. Materials and Methods: In a retrospective study, 21,264 screening digital breast tomosynthesis (DBT) exams obtained at our institution were collected along with associated radiology reports. The 2D synthetic mammographic images from these exams, with varying resolutions and data set sizes, were used to train a multi-view deep …
Hangzhou Hikvision Digital Technology Co., Ltd.（杭州海康威视数字技术股份有限公司）
Background and Objective: Accurate detection of breast masses in mammography images is critical to diagnose early breast cancer, which can greatly improve the patients survival rate. However, it is still a big challenge due to the heterogeneity of breast masses and the complexity of their surrounding environment.Methods: To address these problems, we propose a one-stage object detection architecture, called Breast Mass Detection Network (BMassDNet), based on anchor-free and feature pyramid which makes the detection of breast masses of different sizes well adapted. We introduce a truncation nor…
Institute of Big Data（大数据研究所）；VinUniversity（文大大学）；Hospital 108（108医院）；Hanoi Medical University Hospital（河内医科大学医院）；Yale University（耶鲁大学）
Mammography, or breast X-ray, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assisted detection and diagnosis (CADe or CADx) tools have been developed to support physicians and improve the accuracy of interpreting mammography. However, most published datasets of mammography are either limited on sample size or digitalized from screen-film mammography (SFM), hindering the development of CADe and CADx tools which are developed based on full-field digital mammography (FFDM). To overcome this challeng…
A Multi-Modal AI System for Screening Mammography: Integrating 2D and 3D Imaging to Improve Breast Cancer Detection in a Prospective Clinical Study
Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine（NYU Grossman医学院Vilcek研究生生物医学科学研究所）；NYU Grossman School of Medicine（NYU Grossman医学院）；Center for Data Science, New York University（纽约大学数据科学中心）；Emory University（埃默里大学）；Visage Imaging, Inc.（Visage成像公司）
Purpose: To develop a Breast Imaging Reporting and Data System (BI-RADS) breast density deep learning (DL) model in a multi-site setting for synthetic two-dimensional mammography (SM) images derived from digital breast tomosynthesis exams using full-field digital mammography (FFDM) images and limited SM data. Materials and Methods: A DL model was trained to predict BI-RADS breast density using FFDM images acquired from 2008 to 2017 (Site 1: 57492 patients, 187627 exams, 750752 images) for this retrospective study. The FFDM model was evaluated using SM datasets from two institutions (Site 1: 38…
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）
Computer-aided diagnosis (CAD) has long become an integral part of radiological management of breast disease, facilitating a number of important clinical applications, including quantitative assessment of breast density and early detection of malignancies based on X-ray mammography. Common to such applications is the need to automatically discriminate between breast tissue and adjacent anatomy, with the latter being predominantly represented by pectoralis major (or pectoral muscle). Especially in the case of mammograms acquired in the mediolateral oblique (MLO) view, the muscle is easily confu…
Grading breast density is highly sensitive to normalization settings of digital mammogram as the density is tightly correlated with the distribution of pixel intensity. Also, the grade varies with readers due to uncertain grading criteria. These issues are inherent in the density assessment of digital mammography. They are problematic when designing a computer-aided prediction model for breast density and become worse if the data comes from multiple sites. In this paper, we proposed two novel deep learning techniques for breast density prediction: 1) photometric transformation which adaptively…
Changes in Visual Attention Patterns for Detection Tasks due to Dependencies on Signal and Background Spatial Frequencies
Department of Biomedical Engineering, University of Houston（生物医学工程系，休斯顿大学）；Department of Physics, University of Houston（物理系，休斯顿大学）；Department of Electrical and Computer Engineering, University of Houston（电气与计算机工程系，休斯顿大学）
Breast region segmentation is an essential prerequisite in computerized analysis of mammograms. It aims at separating the breast tissue from the background of the mammogram and it includes two independent segmentations. The first segments the background region which usually contains annotations, labels and frames from the whole breast region, while the second removes the pectoral muscle portion (present in Medio Lateral Oblique (MLO) views) from the rest of the breast tissue. In this paper we propose hybridization of Connected Component Labeling (CCL), Fuzzy, and Straight line methods. Our pro…
I3S - Instituto de Investigação e Inovação em Saúde, Universidade Do Porto（波尔图大学健康创新与研究学院（I3S））；INEB - Instituto de Engenharia Biomédica, Universidade Do Porto（波尔图大学生物医学工程研究所（INEB））；Ipatimup Diagnostics, Institute of Molecular Pathology and Immunology, University of Porto（波尔图大学分子病理学与免疫学研究所Ipatimup诊断中心）；Tongji University（同济大学）；National University of Defense Technology（国防科技大学）；University of Udine（乌迪内大学）；Sharif University of Technology（谢里夫理工大学）；Private Group Practice for Pathology, Lübeck（吕贝克私人病理联合诊所）；DeePathology（DeePathology公司）；Roche Diagnostics GmbH（罗氏诊断有限公司）；Arontier Co., Ltd（Arontier有限公司）；INESC TEC - Institute for Systems and Computer Engineering, Technology and Science（系统与计算机工程技术科学研究所（INESC TEC））；FEUP - Faculty of Engineering, University of Porto（波尔图大学工程学院（FEUP））；FMUP - Faculty of Medicine, University of Porto（波尔图大学医学院（FMUP））
Breast cancer is the most common malignancy in women, being responsible for more than half a million deaths every year. As such, early and accurate diagnosis is of paramount importance. Human expertise is required to diagnose and correctly classify breast cancer and define appropriate therapy, which depends on the evaluation of the expression of different biomarkers such as the transmembrane protein receptor HER2. This evaluation requires several steps, including special techniques such as immunohistochemistry or in situ hybridization to assess HER2 status. With the goal of reducing the number…
Universidad del Caribe（加勒比大学）；Universidad Carlos III de Madrid（马德里卡洛斯三世大学）
Digital mammogram inspection is the most popular technique for early detection of abnormalities in human breast tissue. When mammograms are analyzed through a computational method, the presence of the pectoral muscle might affect the results of breast lesions detection. This problem is particularly evident in the mediolateral oblique view (MLO), where pectoral muscle occupies a large part of the mammography. Therefore, identifying and eliminating the pectoral muscle are essential steps for improving the automatic discrimination of breast tissue. In this paper, we propose an approach based on a…
Sunnybrook Research Institute（桑尼布鲁克研究所）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）
To alleviate the burden of gathering detailed expert annotations when training deep neural networks, we propose a weakly supervised learning approach to recognize metastases in microscopic images of breast lymph nodes. We describe an alternative training loss which clusters weakly labeled bags in latent space to inform relevance of patch-instances during training of a convolutional neural network. We evaluate our method on the Camelyon dataset which contains high-resolution digital slides of breast lymph nodes, where labels are provided at the image-level and only subsets of patches are made a…
Transcend Research Consultancy（transcend研究咨询公司）；School of Computer Sciences, North Maharashtra University（北马哈拉施特拉大学计算机科学学院）
Mammography is a vital screening technique for early revealing and identification of breast cancer in order to assist to decrease mortality rate. Practical applications of mammograms are not limited to breast cancer revealing, identification ,but include task based lens design, image compression, image classification, content based image retrieval and a host of others. Mammography computational analysis methods are a useful tool for specialists to reveal hidden features and extract significant information in mammograms. Digital mammograms are mammography images available along with the convent…
Full Field Digital Mammography Dataset from a Population Screening Program
Faculty of Medicine, Memorial University of Newfoundland（医学学院，纪念大学）；Department of Computer Science, Faculty of Science, Memorial University of Newfoundland（计算机科学系，科学学院，纪念大学）；Cancer Care, Newfoundland and Labrador Health Services（癌症护理，新斯科舍省健康服务）
Siemens Healthineers（西门子医疗）；University Medical Center Goettingen（哥廷根大学医学中心）
Current approaches for classification of whole slide images (WSI) in digital pathology predominantly utilize a two-stage learning pipeline. The first stage identifies areas of interest (e.g. tumor tissue), while the second stage processes cropped tiles from these areas in a supervised fashion. During inference, a large number of tiles are combined into a unified prediction for the entire slide. A major drawback of such approaches is the requirement for task-specific auxiliary labels which are not acquired in clinical routine. We propose a novel learning pipeline for WSI classification that is …
University of São Paulo（圣保罗大学）；Eco & Mama Diagnóstico Digital（Eco & Mama数字诊断公司）
Early detection of breast cancer can increase treatment efficiency. Architectural Distortion (AD) is a very subtle contraction of the breast tissue and may represent the earliest sign of cancer. Since it is very likely to be unnoticed by radiologists, several approaches have been proposed over the years but none using deep learning techniques. To train a Convolutional Neural Network (CNN), which is a deep neural architecture, is necessary a huge amount of data. To overcome this problem, this paper proposes a data augmentation approach applied to clinical image dataset to properly train a CNN. …
Institute of Cancer Research（癌症研究所）；Royal Marsden Hospital（皇家马斯登医院）
Ki67 is an important biomarker for breast cancer. Classification of positive and negative Ki67 cells in histology slides is a common approach to determine cancer proliferation status. However, there is a lack of generalizable and accurate methods to automate Ki67 scoring in large-scale patient cohorts. In this work, we have employed a novel deep learning technique based on hypercolumn descriptors for cell classification in Ki67 images. Specifically, we developed the Simultaneous Detection and Cell Segmentation (DeepSDCS) network to perform cell segmentation and detection. VGG16 network was use…
Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham（阿姆里塔·维什瓦·维迪亚皮塔姆大学无线网络与应用中心）；Valeo（法雷奥）；Department of Computer Science and Engineering, Amrita Vishwa Vidyapeetham（阿姆里塔·维什瓦·维迪亚皮塔姆大学计算机科学与工程系）；School of Computing and Electrical Engineering, IIT Mandi（印度理工学院曼迪分校计算与电气工程学院）
Computer assisted diagnosis in digital pathology is becoming ubiquitous as it can provide more efficient and objective healthcare diagnostics. Recent advances have shown that the convolutional Neural Network (CNN) architectures, a well-established deep learning paradigm, can be used to design a Computer Aided Diagnostic (CAD) System for breast cancer detection. However, the challenges due to stain variability and the effect of stain normalization with such deep learning frameworks are yet to be well explored. Moreover, performance analysis with arguably more efficient network models, which may…
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…
In this paper we examine data fusion methods for multi-view data classification. We present a decision concept which explicitly takes into account the input multi-view structure, where for each case there is a different subset of relevant views. This data fusion concept, which we dub Mixture of Views, is implemented by a special purpose neural network architecture. It is demonstrated on the task of classifying breast microcalcifications as benign or malignant based on CC and MLO mammography views. The single view decisions are combined by a data-driven decision, according to the relevance of e…
Universitat Rovira i Virgili（罗维拉-威尔吉利大学）；Bioinformatics Institute, A*STAR（新加坡科技研究局生物信息学研究所）；Hospital Universitari Sant Joan de Reus（雷乌斯圣琼大学医院）
Mammogram inspection in search of breast tumors is a tough assignment that radiologists must carry out frequently. Therefore, image analysis methods are needed for the detection and delineation of breast masses, which portray crucial morphological information that will support reliable diagnosis. In this paper, we proposed a conditional Generative Adversarial Network (cGAN) devised to segment a breast mass within a region of interest (ROI) in a mammogram. The generative network learns to recognize the breast mass area and to create the binary mask that outlines the breast mass. In turn, the ad…
Beijing Institute of Technology（北京理工大学）；Université 8 Mai 1945 Guelma（盖勒马8 Mai 1945大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer 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肿瘤临床研究中心）
Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segment…
Petuum, Inc.（Petuum公司）；Cornell University（康奈尔大学）；UiT The Arctic University of Norway（挪威北极大学）；Carnegie Mellon University（卡内基梅隆大学）
Convolutional neural networks have led to significant breakthroughs in the domain of medical image analysis. However, the task of breast cancer segmentation in whole-slide images (WSIs) is still underexplored. WSIs are large histopathological images with extremely high resolution. Constrained by the hardware and field of view, using high-magnification patches can slow down the inference process and using low-magnification patches can cause the loss of information. In this paper, we aim to achieve two seemingly conflicting goals for breast cancer segmentation: accurate and fast prediction. We p…
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer
Georgia State University（佐治亚州立大学）；Federal University of Minas Gerais（米纳斯吉拉斯联邦大学）；Winship Cancer Institute（温希普癌症研究所）；Emory University（埃默里大学）
Shenzhen University（深圳大学）；The Ohio State University（俄亥俄州立大学）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
In recent years, advances in the development of whole-slide images have laid a foundation for the utilization of digital images in pathology. With the assistance of computer images analysis that automatically identifies tissue or cell types, they have greatly improved the histopathologic interpretation and diagnosis accuracy. In this paper, the Convolutional Neutral Network (CNN) has been adapted to predict and classify lymph node metastasis in breast cancer. Unlike traditional image cropping methods that are only suitable for large resolution images, we propose a novel data augmentation metho…
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）
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…
Breast cancer has the highest incidence and second highest mortality rate for women in the US. Our study aims to utilize deep learning for benign/malignant classification of mammogram tumors using a subset of cases from the Digital Database of Screening Mammography (DDSM). Though it was a small dataset from the view of Deep Learning (about 1000 patients), we show that currently state of the art architectures of deep learning can find a robust signal, even when trained from scratch. Using convolutional neural networks (CNNs), we are able to achieve an accuracy of 85% and an ROC AUC of 0.91, whi…
