Annotation cost is a bottleneck for collecting massive data in mammography, especially for training deep neural networks. In this paper, we study the use of heterogeneous levels of annotation granularity to improve predictive performances. More precisely, we introduce a multi-task learning scheme for training convolutional neural network (ConvNets), which combines segmentation and classification, using image-level and pixel-level annotations. In this way, different objectives can be used to regularize training by sharing intermediate deep representations. Successful experiments are carried out on the Digital Database of Screening Mammography (DDSM) to validate the relevance of the proposed approach.
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…
Deep learning can promote the mammography-based computer-aided diagnosis (CAD) for breast cancers, but it generally suffers from the small sample size problem. Self-supervised learning (SSL) has shown its effectiveness in medical image analysis with limited training samples. However, the network model sometimes cannot be well pre-trained in the conventional SSL framework due to the limitation of the pretext task and fine-tuning mechanism. In this work, a Task-driven Self-supervised Bi-channel Networks (TSBN) framework is proposed to improve the performance of classification model the mammograp…
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…
Lovely Professional University（洛弗利专业大学）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Maharaja Agrasen Institute of Technology（马哈拉贾·阿格拉森技术学院）；University of Economics Ho Chi Minh City（胡志明市经济大学）
Breast cancer is a common cancer for women. Early detection of breast cancer can considerably increase the survival rate of women. This paper mainly focuses on transfer learning process to detect breast cancer. Modified VGG (MVGG), residual network, mobile network is proposed and implemented in this paper. DDSM dataset is used in this paper. Experimental results show that our proposed hybrid transfers learning model (Fusion of MVGG16 and ImageNet) provides an accuracy of 88.3% where the number of epoch is 15. On the other hand, only modified VGG 16 architecture (MVGG 16) provides an accuracy 8…
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…
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…
Breast cancer is in the most common malignant tumor in women. It accounted for 30% of new malignant tumor cases. Although the incidence of breast cancer remains high around the world, the mortality rate has been continuously reduced. This is mainly due to recent developments in molecular biology technology and improved level of comprehensive diagnosis and standard treatment. Early detection by mammography is an integral part of that. The most common breast abnormalities that may indicate breast cancer are masses and calcifications. Previous detection approaches usually obtain relatively high s…
University of Edinburgh（爱丁堡大学）；Edinburgh Cancer Centre, Western General Hospital（西部综合医院爱丁堡癌症中心）
Computer-aided breast cancer diagnosis in mammography is limited by inadequate data and the similarity between benign and cancerous masses. To address this, we propose a signed graph regularized deep neural network with adversarial augmentation, named \textsc{DiagNet}. Firstly, we use adversarial learning to generate positive and negative mass-contained mammograms for each mass class. After that, a signed similarity graph is built upon the expanded data to further highlight the discrimination. Finally, a deep convolutional neural network is trained by jointly optimizing the signed graph regula…
Transformer-Based Explainable Deep Learning for Breast Cancer Detection in Mammography: The MammoFormer Framework
Morgan State University（摩根州立大学）；Wrexham University（雷克瑟姆大学）；Federal University of Technology, Minna（明纳联邦理工大学）；Federal University of Education, Kano（卡诺联邦教育大学）
Center for Informatics, Federal University of Pernambuco（伯南布哥联邦大学信息学中心）；Department of Biomedical Engineering, Federal University of Pernambuco（伯南布哥联邦大学生物医学工程系）
Breast cancer is already one of the most common form of cancer worldwide. Mammography image analysis is still the most effective diagnostic method to promote the early detection of breast cancer. Accurately segmenting tumors in digital mammography images is important to improve diagnosis capabilities of health specialists and avoid misdiagnosis. In this work, we evaluate the feasibility of applying GrowCut to segment regions of tumor and we propose two GrowCut semi-supervised versions. All the analysis was performed by evaluating the application of segmentation techniques to a set of images ob…
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）
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）
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…
University of Idaho（爱达荷大学）；Harbin Institute of Technology（哈尔滨工业大学）；Medical College at Qingdao University（青岛大学医学院）
Deep learning-based computer-aided diagnosis has achieved unprecedented performance in breast cancer detection. However, most approaches are computationally intensive, which impedes their broader dissemination in real-world applications. In this work, we propose an efficient and light-weighted multitask learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. Moreover, we propose a new numerically stable loss function that e…
University of Edinburgh（爱丁堡大学）；Western General Hospital（西部综合医院）
Deep convolutional neural networks (CNNs) have emerged as a new paradigm for Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis (CAD) for breast cancer directly extract latent features from input mammogram image and ignore the importance of morphological features. In this paper, we introduce a novel deep learning framework for mammogram image processing, which computes mass segmentation and simultaneously predict diagnosis results. Specifically, our method is constructed in a dual-path architecture that solves the mapping in a dual-problem manner, with an additional considera…
Escola Politécnica, Universidade de São Paulo（圣保罗大学理工学院）；Instituto do Câncer do Estado de São Paulo（圣保罗州癌症研究所）；Faculdade de Medicina, Universidade de São Paulo（圣保罗大学医学院）
Some recent studies have described deep convolutional neural networks to diagnose breast cancer in mammograms with similar or even superior performance to that of human experts. One of the best techniques does two transfer learnings: the first uses a model trained on natural images to create a "patch classifier" that categorizes small subimages; the second uses the patch classifier to scan the whole mammogram and create the "single-view whole-image classifier". We propose to make a third transfer learning to obtain a "two-view classifier" to use the two mammographic views: bilateral craniocaud…
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital（马萨诸塞州总医院阿西诺拉·A·马丁诺斯生物医学成像中心）；Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；University of Colorado School of Medicine（科罗拉多大学医学院）；Harvard Medical School（哈佛医学院）
Breast cancer is the most common cancers and early detection from mammography screening is crucial in improving patient outcomes. Assessing mammographic breast density is clinically important as the denser breasts have higher risk and are more likely to occlude tumors. Manual assessment by experts is both time-consuming and subject to inter-rater variability. As such, there has been increased interest in the development of deep learning methods for mammographic breast density assessment. Despite deep learning having demonstrated impressive performance in several prediction tasks for applicatio…
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）
Early detection of breast cancer in mammography screening via deep-learning based computer-aided detection systems shows promising potential in improving the curability and mortality rates of breast cancer. However, many clinical centres are restricted in the amount and heterogeneity of available data to train such models to (i) achieve promising performance and to (ii) generalise well across acquisition protocols and domains. As sharing data between centres is restricted due to patient privacy concerns, we propose a potential solution: sharing trained generative models between centres as subs…
School of Electrical Engineering, Tel-Aviv University（特拉维夫大学电气工程学院）；The Manuel and Raquel Klachky Chair of Image Processing, School of Electrical Engineering, Tel-Aviv University（特拉维夫大学电气工程学院 Manuel and Raquel Klachky 图像处理教授职位）；Diagnostic Imaging, Sheba Medical Center, affiliated to the Sackler School of Medicine, Tel-Aviv University（舍巴医疗中心诊断影像科，隶属于特拉维夫大学萨克勒医学院）
Mammography and ultrasound are extensively used by radiologists as complementary modalities to achieve better performance in breast cancer diagnosis. However, existing computer-aided diagnosis (CAD) systems for the breast are generally based on a single modality. In this work, we propose a deep-learning based method for classifying breast cancer lesions from their respective mammography and ultrasound images. We present various approaches and show a consistent improvement in performance when utilizing both modalities. The proposed approach is based on a GoogleNet architecture, fine-tuned for o…
Computer-aided detection systems based on deep learning have shown great potential in breast cancer detection. However, the lack of domain generalization of artificial neural networks is an important obstacle to their deployment in changing clinical environments. In this work, we explore the domain generalization of deep learning methods for mass detection in digital mammography and analyze in-depth the sources of domain shift in a large-scale multi-center setting. To this end, we compare the performance of eight state-of-the-art detection methods, including Transformer-based models, trained i…
Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography
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）；São Carlos School of Engineering, University of São Paulo（圣保罗大学圣卡洛斯工程学院）；Ribeirão Preto School of Medicine, University of São Paulo（圣保罗大学里贝朗普雷图医学院）
Early detection of breast cancer through screening mammography yields a 20-35% increase in survival rate; however, there are not enough radiologists to serve the growing population of women seeking screening mammography. Although commercial computer aided detection (CADe) software has been available to radiologists for decades, it has failed to improve the interpretation of full-field digital mammography (FFDM) images due to its low sensitivity over the spectrum of findings. In this work, we leverage a large set of FFDM images with loose bounding boxes of mammographically significant findings …
A Hybrid CNN-VSSM model for Multi-View, Multi-Task Mammography Analysis: Robust Diagnosis with Attention-Based Fusion
Department of Mathematics and Statistics, Qatar University（数学与统计学系，卡塔尔大学）；Department of Computer Science and Engineering, Qatar University（计算机科学与工程系，卡塔尔大学）；Department of Electrical Engineering, Qatar University（电气工程系，卡塔尔大学）；Graduate School of Information Science, University of Hyogo（信息科学研究生院，广岛大学）；Advanced Medical Engineering Research Institute, University of Hyogo（先进医疗工程研究所，广岛大学）
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…
Leibniz University Hannover（汉诺威莱布尼茨大学）；Memorial University of Newfoundland（纽芬兰纪念大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Guangdong Pharmaceutical University（广东药科大学）；McGill University（麦吉尔大学）；University of Milano-Bicocca（米兰比可卡大学）；University of Southern Queensland（南昆士兰大学）
Breast cancer is the second leading cause of cancer-related death after lung cancer in women. Early detection of breast cancer in X-ray mammography is believed to have effectively reduced the mortality rate. However, a relatively high false positive rate and a low specificity in mammography technology still exist. In this work, a computer-aided automatic mammogram analysis system is proposed to process the mammogram images and automatically discriminate them as either normal or cancerous, consisting of three consecutive image processing, feature selection, and image classification stages. In d…
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 …
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…
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）
Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography
Concordia Institute for Information Systems Engineering (CIISE)（康科德信息系统工程研究所）；Concordia University（康科德大学）；Thomas Jefferson University Hospital（托马斯·杰斐逊大学医院）
BreastMammo and DenseMammo: Benchmarks for Mammography Domain Generalization
Northwestern University（西北大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Istanbul University（伊斯坦布尔大学）；Namik Kemal University（纳米克·凯末尔大学）；Istanbul Florence Nightingale Hospital（伊斯坦布尔佛罗伦萨南丁格尔医院）；Acibadem Mehmet Ali Aydinlar University（阿西巴德姆·穆罕默德·阿里·艾丁拉尔大学）；Qatar University（卡塔尔大学）；Boston University（波士顿大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；NVIDIA（英伟达公司）
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…
Universidad Técnica Particular de Loja（洛哈技术大学）；Instituto de Instrumentacion para la Imagen Molecular I3M, Universitat Politécnica de Valencia（瓦伦西亚理工大学分子图像仪器研究所I3M）；University of Waterloo（滑铁卢大学）
This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis (CAD) systems, which make use of new deep learning methods to automatically recognize images and improve the accuracy of diagnosis made by radiologists. This review is based upon published literature in the past decade (January 2010 January 2020). The main findings in the classification process reveal that new DL-CAD methods are useful and effective screening tools for breast cance…
Background \& purpose: The recent emergence of neural networks models for the analysis of breast images has been a breakthrough in computer aided diagnostic. This approach was not yet developed in Contrast Enhanced Spectral Mammography (CESM) where access to large databases is complex. This work proposes a deep-learning-based Computer Aided Diagnostic development for CESM recombined images able to detect lesions and classify cases. Material \& methods: A large CESM diagnostic dataset with biopsy-proven lesions was collected from various hospitals and different acquisition systems. The annotate…
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…
Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer Using Generative Artificial Intelligence
Department of Radiology, Fondazione Policlinico Campus Bio-Medico（放射学系，政策临床医学院）；Department of Radiology, Università Campus Bio-Medico di Roma（放射学系，罗马生物医学大学）；Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University（诊断与介入系，辐射物理，生物医学工程，乌梅拉大学）
