Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms
Center for Data Science, New York University（纽约大学数据科学中心）；Department of Radiology, New York University School of Medicine（纽约大学医学院放射科）；Perlmutter Cancer Center, NYU Langone Health（纽约大学朗格尼医学中心珀尔马特癌症中心）；Center for Advanced Imaging Innovation and Research, NYU Langone Health（纽约大学朗格尼医学中心先进成像创新与研究中心）；Department of Computer Science, Courant Institute of Mathematical Sciences, New York University（纽约大学库朗数学科学研究所计算机科学系）
Nan Wu, Zhe Huang, Yiqiu Shen, Jungkyu Park, Jason Phang, Taro Makino, S. Gene Kim, Kyunghyun Cho, Laura Heacock, Linda Moy, Krzysztof J. Geras
Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate of biopsies that turn out to be benign tissue. In this study, we build deep neural networks (DNNs) to classify biopsied lesions as being either malignant or benign, with the goal of using these networks as second readers serving radiologists to further reduce the number of false positive findings. We enhance the performance of DNNs that are trained to learn from small image patches by integrating global context provided in the form of saliency maps learned from the entire image into their reasoning, similar to how radiologists consider global context when evaluating areas of interest. Our experiments are conducted on a dataset of 229,426 screening mammography exams from 141,473 patients. We achieve an AUC of 0.8 on a test set consisting of 464 benign and 136 malignant lesions.
New York University（纽约大学）；New York University School of Medicine（纽约大学医学院）
Radiologists typically compare a patient's most recent breast cancer screening exam to their previous ones in making informed diagnoses. To reflect this practice, we propose new neural network models that compare pairs of screening mammograms from the same patient. We train and evaluate our proposed models on over 665,000 pairs of images (over 166,000 pairs of exams). Our best model achieves an AUC of 0.866 in predicting malignancy in patients who underwent breast cancer screening, reducing the error rate of the corresponding baseline.
Emory University（埃默里大学）；Georgia Institute of Technology（佐治亚理工学院）；Kennesaw State University（肯尼索州立大学）
Developing and validating artificial intelligence models in medical imaging requires datasets that are large, granular, and diverse. To date, the majority of publicly available breast imaging datasets lack in one or more of these areas. Models trained on these data may therefore underperform on patient populations or pathologies that have not previously been encountered. The EMory BrEast imaging Dataset (EMBED) addresses these gaps by providing 3650,000 2D and DBT screening and diagnostic mammograms for 116,000 women divided equally between White and African American patients. The dataset also…
University of Houston（休斯顿大学）；Houston Methodist（休斯顿卫理公会医院）
Accurate breast lesion risk estimation can significantly reduce unnecessary biopsies and help doctors decide optimal treatment plans. Most existing computer-aided systems rely solely on mammogram features to classify breast lesions. While this approach is convenient, it does not fully exploit useful information in clinical reports to achieve the optimal performance. Would clinical features significantly improve breast lesion classification compared to using mammograms alone? How to handle missing clinical information caused by variation in medical practice? What is the best way to combine mamm…
Our objective is to show the feasibility of using simulated mammograms to detect mammographically-occult (MO) cancer in women with dense breasts and a normal screening mammogram who could be triaged for additional screening with magnetic resonance imaging (MRI) or ultrasound. We developed a Conditional Generative Adversarial Network (CGAN) to simulate a mammogram with normal appearance using the opposite mammogram as the condition. We used a Convolutional Neural Network (CNN) trained on Radon Cumulative Distribution Transform (RCDT) processed mammograms to detect MO cancer. For training CGAN, …
Data scarcity and class imbalance are two fundamental challenges in many machine learning applications to healthcare. Breast cancer classification in mammography exemplifies these challenges, with a malignancy rate of around 0.5% in a screening population, which is compounded by the relatively small size of lesions (~1% of the image) in malignant cases. Simultaneously, the prevalence of screening mammography creates a potential abundance of non-cancer exams to use for training. Altogether, these characteristics lead to overfitting on cancer cases, while under-utilizing non-cancer data. Here, w…
University of Girona（赫罗纳大学）；University of Burgundy（勃艮第大学）
An advanced reliable low-cost form of screening method, Digital mammography has been used as an effective imaging method for breast cancer detection. With an increased focus on technologies to aid healthcare, Mammogram images have been utilized in developing computer-aided diagnosis systems that will potentially help in clinical diagnosis. Researchers have proved that artificial intelligence with its emerging technologies can be used in the early detection of the disease and improve radiologists' performance in assessing breast cancer. In this paper, we review the methods developed for mammogr…
A key promise of AI applications in healthcare is in increasing access to quality medical care in under-served populations and emerging markets. However, deep learning models are often only trained on data from advantaged populations that have the infrastructure and resources required for large-scale data collection. In this paper, we aim to empirically investigate the potential impact of such biases on breast cancer detection in mammograms. We specifically explore how a deep learning algorithm trained on screening mammograms from the US and UK generalizes to mammograms collected at a hospital…
Mayo Clinic（梅奥诊所）；Ohio State University（俄亥俄州立大学）；West Virginia University School of Medicine（西弗吉尼亚大学医学院）；NVIDIA Inc.（英伟达公司）
Screening mammograms are a routine imaging exam performed to detect breast cancer in its early stages to reduce morbidity and mortality attributed to this disease. In order to maximize the efficacy of breast cancer screening programs, proper mammographic positioning is paramount. Proper positioning ensures adequate visualization of breast tissue and is necessary for effective breast cancer detection. Therefore, breast-imaging radiologists must assess each mammogram for the adequacy of positioning before providing a final interpretation of the examination; this often necessitates return patient…
Radboud University Medical Center（拉德堡德大学医学中心）；Ospedale Cannizzaro（坎尼扎罗医院）；Leids Universitair Medisch Centrum（莱顿大学医学中心）；University of Bayreuth（拜罗伊特大学）；Linköping University（林雪平大学）
Artificial Intelligence (AI) can potentially support histopathologists in the diagnosis of a broad spectrum of cancer types. In colorectal cancer (CRC), AI can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs, ongoing in many countries all around the globe. Here, we present an approach to address two major challenges in automated assessment of CRC histopathology whole-slide images. First, we present an AI-based method to segment multiple tissue compartments…
Ordinal Learning: Longitudinal Attention Alignment Model for Predicting Time to Future Breast Cancer Events from Mammograms
Netherlands Cancer Institute (NKI)（荷兰癌症研究所）；GROW School for Oncology and Development Biology, Maastricht University（马斯特里赫特大学GROW肿瘤学与发育生物学学院）；Macao Polytechnic University（澳门理工大学）；Radboud University Medical Centre（拉德堡德大学医学中心）
Breast density estimation is one of the key tasks in recognizing individuals predisposed to breast cancer. It is often challenging because of low contrast and fluctuations in mammograms' fatty tissue background. Most of the time, the breast density is estimated manually where a radiologist assigns one of the four density categories decided by the Breast Imaging and Reporting Data Systems (BI-RADS). There have been efforts in the direction of automating a breast density classification pipeline. Breast density estimation is one of the key tasks performed during a screening exam. Dense breasts ar…
Advanced deep learning (DL) algorithms may predict the patient's risk of developing breast cancer based on the Breast Imaging Reporting and Data System (BI-RADS) and density standards. Recent studies have suggested that the combination of multi-view analysis improved the overall breast exam classification. In this paper, we propose a novel multi-view DL approach for BI-RADS and density assessment of mammograms. The proposed approach first deploys deep convolutional networks for feature extraction on each view separately. The extracted features are then stacked and fed into a Light Gradient Boo…
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…
Multivariate Analysis on Performance Gaps of Artificial Intelligence Models in Screening Mammography
Kennesaw State University（肯尼索州立大学）；Emory University（埃默里大学）；Washington University in St. Louis School of Medicine（圣路易斯华盛顿大学医学院）；Mayo Clinic Arizona（梅奥诊所亚利桑那分院）；Arizona State University（亚利桑那州立大学）；York University（约克大学）
VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH（VRVis虚拟现实与可视化研究中心有限公司）
Machine learning and deep learning methods have become essential for computer-assisted prediction in medicine, with a growing number of applications also in the field of mammography. Typically these algorithms are trained for a specific task, e.g., the classification of lesions or the prediction of a mammogram's pathology status. To obtain a comprehensive view of a patient, models which were all trained for the same task(s) are subsequently ensembled or combined. In this work, we propose a pipeline approach, where we first train a set of individual, task-specific models and subsequently invest…
Breast cancer is a disease that threatens many women's life, thus, early and accurate detection plays a key role in reducing the mortality rate. Mammography stands as the reference technique for breast cancer screening; nevertheless, many countries still lack access to mammograms due to economic, social, and cultural issues. Last advances in computational tools, infrared cameras, and devices for bio-impedance quantification allowed the development of parallel techniques like thermography, infrared imaging, and electrical impedance tomography, these being faster, reliable and cheaper. In the la…
RADIFUSION: A multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement
Monash University Malaysia（莫纳什大学马来西亚分校）；The University of Oklahoma（俄克拉荷马大学）；Karolinska Institute（卡罗林斯卡学院）；Karolinska University Hospital（卡罗林斯卡大学医院）；University of Malaya（马来亚大学）；The University of Melbourne（墨尔本大学）
Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Kaiser Permanente Northern California, Division of Research（北加州凯撒永久医疗集团研究部）
A major limitation in applying deep learning to artificial intelligence (AI) systems is the scarcity of high-quality curated datasets. We investigate strong augmentation based self-supervised learning (SSL) techniques to address this problem. Using breast cancer detection as an example, we first identify a mammogram-specific transformation paradigm and then systematically compare four recent SSL methods representing a diversity of approaches. We develop a method to convert a pretrained model from making predictions on uniformly tiled patches to whole images, and an attention-based pooling meth…
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 …
University of Pennsylvania（宾夕法尼亚大学）；Mayo Clinic（梅奥诊所）
Breast density is an important risk factor for breast cancer that also affects the specificity and sensitivity of screening mammography. Current federal legislation mandates reporting of breast density for all women undergoing breast screening. Clinically, breast density is assessed visually using the American College of Radiology Breast Imaging Reporting And Data System (BI-RADS) scale. Here, we introduce an artificial intelligence (AI) method to estimate breast percentage density (PD) from digital mammograms. Our method leverages deep learning (DL) using two convolutional neural network arch…
Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction
Department of Physics and Technology, UiT The Arctic University of Norway（物理与技术系，UiT北极大学）；SPKI The Norwegian Centre for Clinical Artificial Intelligence（挪威临床人工智能中心）；Department of Computer Science and Engineering, University of Bologna（计算机科学与工程系，博洛尼亚大学）；Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering IMTE（个体化和细胞基础医学工程研究机构IMTE）；Norwegian Computing Center, Oslo, Norway（挪威计算中心，奥斯陆，挪威）；Pioneer Centre for AI, University of Copenhagen（人工智能先锋中心，哥本哈根大学）
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…
Screening mammograms is the gold standard for detecting breast cancer early. While a good amount of work has been performed on mammography image classification, especially with deep neural networks, there has not been much exploration into the confidence or uncertainty measurement of the classification. In this paper, we propose a confidence measure-based evaluation metric for breast cancer screening. We propose a modular network architecture, where a traditional neural network is used as a feature extractor with transfer learning, followed by a simple Bayesian neural network. Utilizing a two-…
STA-Risk: A Deep Dive of Spatio-Temporal Asymmetries for Breast Cancer Risk Prediction
Intelligent Systems Program, University of Pittsburgh（匹兹堡大学智能系统项目）；Department of Radiology, University of Pittsburgh（匹兹堡大学放射科）；Department of Biomedical Informatics and Department of Bioengineering University of Pittsburgh（匹兹堡大学生物医学信息学系和生物工程系）
Deep Learning-Based Breast Cancer Detection in Mammography: A Multi-Center Validation Study in Thai Population
Perceptra Co., Ltd（Perceptra公司）；Institute of Field Robotics（现场机器人研究所）；King Mongkut’s University of Technology Thonburi（技术师范大学）；Mahidol University（玛希多大学）；Siriraj Hospital（Siriraj医院）；Khonkaen University（孔卡恩大学）
A major barrier to medical imaging research and in particular the development of artificial intelligence (AI) is a lack of large databases of medical images which share images with other researchers. Without such databases it is not possible to train generalisable AI algorithms, and large amounts of time and funding is spent collecting smaller datasets at individual research centres. The OPTIMAM image database (OMI-DB) has been developed to overcome these barriers. OMI-DB consists of several relational databases and cloud storage systems, containing mammography images and associated clinical a…
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
The Hong Kong University of Science and Technology（香港科技大学）；Stanford University School of Medicine（斯坦福大学医学院）；The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳-香港协同创新研究院）
AI-assisted prostate cancer detection and localisation on biparametric MR by classifying radiologist-positives
Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London（医学图像计算中心，医学物理与生物医学工程系，伦敦大学学院）；Div of Surgery & Interventional Sci, University College London（外科与介入科学部，伦敦大学学院）；School of Computer Science and Engineering, University of Westminster（计算机科学与工程学院，威斯敏斯特大学）
Mammographic Breast Positioning Assessment via Deep Learning
Istanbul Technical University（伊斯坦布尔理工大学）；Acibadem Healthcare Group（阿西巴德姆医疗集团）；Acibadem Mehmet Ali Aydinlar University（阿西巴德姆穆罕默德·阿里·艾丁拉尔大学）；Sisli Hamidiye Etfal Training and Research Hospital（锡什利哈米迪耶埃特法尔培训与研究医院）
