Invasive ductal carcinoma (IDC), which is also sometimes known as the infiltrating ductal carcinoma, is the most regular form of breast cancer. It accounts for about 80% of all breast cancers. According to the American Cancer Society, more than 180,000 women in the United States are diagnosed with invasive breast cancer each year. The survival rate associated with this form of cancer is about 77% to 93% depending on the stage at which they are being diagnosed. The invasiveness and the frequency of the occurrence of these disease makes it one of the difficult cancers to be diagnosed. Our proposed methodology involves diagnosing the invasive ductal carcinoma with a deep residual convolution network to classify the IDC affected histopathological images from the normal images. The dataset for the purpose used is a benchmark dataset known as the Breast Histopathology Images. The microscopic RGB images are converted into a seven channel image matrix, which is then fed to the network. The proposed model produces a 99.29% accurate approach towards the prediction of IDC in the histopathology images with an AUROC score of 0.9996. Classification ability of the model is tested using standard performance metrics.
We propose a virtual staining methodology based on Generative Adversarial Networks to map histopathology images of breast cancer tissue from H&E stain to PHH3 and vice versa. We use the resulting synthetic images to build Convolutional Neural Networks (CNN) for automatic detection of mitotic figures, a strong prognostic biomarker used in routine breast cancer diagnosis and grading. We propose several scenarios, in which CNN trained with synthetically generated histopathology images perform on par with or even better than the same baseline model trained with real images. We discuss the potentia…
Northeastern University（东北大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Cancer Hospital of China Medical University（中国医科大学附属肿瘤医院）；Chengdu University of Information Technology（成都信息工程大学）
Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems int…
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）
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…
Radboud University Medical Center（拉德堡德大学医学中心）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard Medical School（哈佛医学院）；National Cancer Institute（美国国家癌症研究所）；The Harker School（哈克学校）；Mayo Clinic（梅奥诊所）
Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture. Consequently, most of the automated systems have focused on characterizing the epithelial regions of the breast to detect cancer. In this paper, we propose a system for classification of hematoxylin and eosin (H&E) stained breast specimens based on convolutional neural networks that primarily targets the assessment of tumor-associated stroma to diagnose breast cancer patients. We evaluate the performance of our proposed system usin…
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…
A Chain-of-thought Reasoning Breast Ultrasound Dataset Covering All Histopathology Categories
Peking University（北京大学）；Peking Union Medical College Hospital（北京大学人民医院）；Peking University Cancer Hospital & Institute（北京大学肿瘤医院）；National Cancer Center / National Clinical Research Center for Cancer / Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院国家癌症中心/国家癌症临床科研中心/癌症医院）；Shenzhen Maternity & Child Health Care Hospital（深圳妇幼保健医院）；Xi’an Jiaotong University（西安交通大学）；Yizhun Medical AI Co., Ltd（亿众医疗人工智能有限公司）
We present a unified framework to predict tumor proliferation scores from breast histopathology whole slide images. Our system offers a fully automated solution to predicting both a molecular data-based, and a mitosis counting-based tumor proliferation score. The framework integrates three modules, each fine-tuned to maximize the overall performance: An image processing component for handling whole slide images, a deep learning based mitosis detection network, and a proliferation scores prediction module. We have achieved 0.567 quadratic weighted Cohen's kappa in mitosis counting-based score p…
University Joseph Fourier（约瑟夫·傅里叶大学）；IPAL, CNRS UMI 2955（IPAL，法国国家科学研究中心UMI 2955）；CoSMo Software（CoSMo软件公司）；Temasys Communications（Temasys通信公司）；University Pierre and Marie Curie（皮埃尔和玛丽·居里大学）
Accurate detection of mitosis plays a critical role in breast cancer histopathology. Manual detection and counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. Multispectral imaging is a recent medical imaging technology, proven successful in increasing the segmentation accuracy in other fields. This study aims at improving the accuracy of mitosis detection by developing a specific solution using multispectral and multifocal imaging of breast cancer histopathological data. We propose to enable clinical routine-compliant quality of mitosis discrimination…
This work presents a novel self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors. Other state-of-theart works mainly focus on fully supervised learning approaches that rely heavily on human annotations. However, the scarcity of labeled and unlabeled data is a long-standing challenge in histopathology. Currently, representation learning without labels remains unexplored for the histopathology domain. The proposed method, Magnification Prior Contrastive Similarity (MPCS), enables self-supervised lear…
Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area. In this paper, we present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). We first train a CNN using high pixel resolution patches to capture cellular level information. The feature responses generated by this model are then fed as input to a second CNN, stacked on top of the first. Training of this stac…
Studies estimate that there will be 266,120 new cases of invasive breast cancer and 40,920 breast cancer induced deaths in the year of 2018 alone. Despite the pervasiveness of this affliction, the current process to obtain an accurate breast cancer prognosis is tedious and time consuming, requiring a trained pathologist to manually examine histopathological images in order to identify the features that characterize various cancer severity levels. We propose MITOS-RCNN: a novel region based convolutional neural network (RCNN) geared for small object detection to accurately grade one of the thre…
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）
Karolinska Institutet（卡罗林斯卡学院）；Tampere University（坦佩雷大学）；Lund University（隆德大学）；University of Turku（图尔库大学）；Karolinska University Laboratory（卡罗林斯卡大学实验室）
Molecular phenotyping is central in cancer precision medicine, but remains costly and standard methods only provide a tumour average profile. Microscopic morphological patterns observable in histopathology sections from tumours are determined by the underlying molecular phenotype and associated with clinical factors. The relationship between morphology and molecular phenotype has a potential to be exploited for prediction of the molecular phenotype from the morphology visible in histopathology images. We report the first transcriptome-wide Expression-MOrphology (EMO) analysis in breast cancer,…
Breast cancer has the highest mortality among cancers in women. Computer-aided pathology to analyze microscopic histopathology images for diagnosis with an increasing number of breast cancer patients can bring the cost and delays of diagnosis down. Deep learning in histopathology has attracted attention over the last decade of achieving state-of-the-art performance in classification and localization tasks. The convolutional neural network, a deep learning framework, provides remarkable results in tissue images analysis, but lacks in providing interpretation and reasoning behind the decisions. …
Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images
Artificial Intelligence Department, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran；Department of Computer Engineering Techniques, Mazaya University College, Nasiriyah, Iraq；Department of Medicine, Danube Private University, Krems an der Donau, Austria；Austrian Center for Medical Innovation；Research Center for Medical Image Analysis；Artificial Intelligence, Department of Medicine, Danube Private University, Krems an der Donau, Austria
Eindhoven University of Technology（埃因霍温理工大学）；University Medical Center Utrecht（乌得勒支大学医学中心）；University Utrecht（乌得勒支大学）；Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed a deep learning-based DCIS grading sys…
University of Warwick（华威大学）；Tissue Image Analytics (TIA) Centre, University of Warwick（华威大学组织图像分析中心）
The problem of recognizing various types of tissues present in multi-gigapixel histology images is an important fundamental pre-requisite for downstream analysis of the tumor microenvironment in a bottom-up analysis paradigm for computational pathology. In this paper, we propose a deep dictionary learning approach to solve the problem of tissue phenotyping in histology images. We propose deep Multi-Resolution Dictionary Learning (deepMRDL) in order to benefit from deep texture descriptors at multiple different spatial resolutions. We show the efficacy of the proposed approach through extensive…
Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院阿蒂努拉·A·马蒂诺斯生物医学成像中心）；Faculty of Engineering, University of Porto（波尔图大学工程学院）；Institute for Systems and Computer Engineering, Technology and Science (INESC TEC)（系统与计算机工程、技术与科学研究院）
Multi-modal Knowledge Decomposition based Online Distillation for Biomarker Prediction in Breast Cancer Histopathology
School of Biomedical Engineering, Faulty of Medicine, Dalian University of Technology, Dalian, China（生物医学工程学院）；Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, Finland（信息科技学院）；School of Software Technology, Dalian University of Technology, Dalian, China（软件技术学院）；Department of Radiology, Guangdong Provincial People’s Hospital, Southern Medical University, Guangzhou, China（放射科）
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…
In recent years, histopathology images have been increasingly used as a diagnostic tool in the medical field. The process of accurately diagnosing a biopsy sample requires significant expertise in the field, and as such can be time-consuming and is prone to uncertainty and error. With the advent of digital pathology, using image recognition systems to highlight problem areas or locate similar images can aid pathologists in making quick and accurate diagnoses. In this paper, we specifically consider the encoded local projections (ELP) algorithm, which has previously shown some success as a tool…
Northeastern University（东北大学）；China Medical University（中国医科大学）；University of Luebeck（吕贝克大学）
Background: Breast cancer has the highest prevalence in women globally. The classification and diagnosis of breast cancer and its histopathological images have always been a hot spot of clinical concern. In Computer-Aided Diagnosis (CAD), traditional classification models mostly use a single network to extract features, which has significant limitations. On the other hand, many networks are trained and optimized on patient-level datasets, ignoring the application of lower-level data labels. Method: This paper proposes a deep ensemble model based on image-level labels for the binary classificat…
It is commonly recognized that color variations caused by differences in stains is a critical issue for histopathology image analysis. Existing methods adopt color matching, stain separation, stain transfer or the combination of them to alleviate the stain variation problem. In this paper, we propose a novel Stain-Adaptive Self-Supervised Learning(SASSL) method for histopathology image analysis. Our SASSL integrates a domain-adversarial training module into the SSL framework to learn distinctive features that are robust to both various transformations and stain variations. The proposed SASSL i…
Automating tumor-infiltrating lymphocyte assessment in breast cancer histopathology images using QuPath: a transparent and accessible machine learning pipeline
UiT The Arctic University of Norway（挪威北极圈大学）；University Hospital of North Norway（挪威北部大学医院）
Despite the promise of Convolutional neural network (CNN) based classification models for histopathological images, it is infeasible to quantify its uncertainties. Moreover, CNNs may suffer from overfitting when the data is biased. We show that Bayesian-CNN can overcome these limitations by regularizing automatically and by quantifying the uncertainty. We have developed a novel technique to utilize the uncertainties provided by the Bayesian-CNN that significantly improves the performance on a large fraction of the test data (about 6% improvement in accuracy on 77% of test data). Further, we pr…
Indian Institute of Technology Bombay（印度理工学院孟买分校）；University of Illinois（伊利诺伊大学）
Spatial arrangement of cells of various types, such as tumor infiltrating lymphocytes and the advancing edge of a tumor, are important features for detecting and characterizing cancers. However, convolutional neural networks (CNNs) do not explicitly extract intricate features of the spatial arrangements of the cells from histopathology images. In this work, we propose to classify cancers using graph convolutional networks (GCNs) by modeling a tissue section as a multi-attributed spatial graph of its constituent cells. Cells are detected using their nuclei in H&E stained tissue image, and each …
In this paper, we develop a complete pipeline for stain normalization, segmentation, and classification of nuclei in hematoxylin and eosin (H&E) stained breast cancer histopathology images. In the first step, we use a CNN-based stain transfer technique to normalize the staining characteristics of (H&E) images. We then train a neural network to segment images of nuclei from the H&E images. Finally, we train an Information Maximizing Generative Adversarial Network (InfoGAN) to learn visual representations of different types of nuclei and classify them in an entirely unsupervised manner. The resu…
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…
The size of nuclei in histological preparations from excised breast tumors is predictive of patient outcome (large nuclei indicate poor outcome). Pathologists take into account nuclear size when performing breast cancer grading. In addition, the mean nuclear area (MNA) has been shown to have independent prognostic value. The straightforward approach to measuring nuclear size is by performing nuclei segmentation. We hypothesize that given an image of a tumor region with known nuclei locations, the area of the individual nuclei and region statistics such as the MNA can be reliably computed direc…
University of Geneva (UNIGE)（日内瓦大学）；University of Applied Sciences Western Switzerland (HES-SO)（瑞士西部应用科学大学）
Explanations for deep neural network predictions in terms of domain-related concepts can be valuable in medical applications, where justifications are important for confidence in the decision-making. In this work, we propose a methodology to exploit continuous concept measures as Regression Concept Vectors (RCVs) in the activation space of a layer. The directional derivative of the decision function along the RCVs represents the network sensitivity to increasing values of a given concept measure. When applied to breast cancer grading, nuclei texture emerges as a relevant concept in the detecti…
While microscopic analysis of histopathological slides is generally considered as the gold standard method for performing cancer diagnosis and grading, the current method for analysis is extremely time consuming and labour intensive as it requires pathologists to visually inspect tissue samples in a detailed fashion for the presence of cancer. As such, there has been significant recent interest in computer aided diagnosis systems for analysing histopathological slides for cancer grading to aid pathologists to perform cancer diagnosis and grading in a more efficient, accurate, and consistent ma…
Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review
McMaster University（麦克马斯特大学）；Mayo Clinic（梅奥诊所）；William Osler Health System（威廉·奥斯勒医疗系统）
Histopathology image embedding is an active research area in computer vision. Most of the embedding models exclusively concentrate on a specific magnification level. However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level. Two main approaches for tackling this goal are domain adaptation and domain generalization, where the target magnification levels may or may not be introduced to the model in training, respectively. Although magnification adaptation is a well-studied topic in the literature, this paper, to the best of our knowle…
University of Memphis（孟菲斯大学）；Microsoft Research（微软研究院）；AutoML Image Lab（AutoML图像实验室）
Breast Cancer is the most prevalent cancer in the world. The World Health Organization reports that the disease still affects a significant portion of the developing world citing increased mortality rates in the majority of low to middle income countries. The most popular protocol pathologists use for diagnosing breast cancer is the Nottingham grading system which grades the proliferation of tumors based on 3 major criteria, the most important of them being mitotic cell count. The way in which pathologists evaluate mitotic cell count is to subjectively and qualitatively analyze cells present i…
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…
Addressing Uncertainty in Imbalanced Histopathology Image Classification of HER2 Breast Cancer: An interpretable Ensemble Approach with Threshold Filtered Single Instance Evaluation (SIE)
American International University-Bangladesh（孟加拉美国国际大学）；Bangladesh University of Business and Technology（孟加拉商业技术大学）；King Saud University（沙特国王大学）；Southern Illinois University（南伊利诺伊大学）
