Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging

Cluster of microcalcifications can be an early sign of breast cancer. In this paper we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work we used 283 mammograms to train and validate our model, obtaining an accuracy of 98.22% in the detection of preliminary suspect regions and of 97.47% in the segmentation task. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.Comment: 13 pages, 7 figure

Abnormality Detection in Mammography using Deep Convolutional Neural Networks

Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53\% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.Comment: 6 page

Localization of Microcalcification on the Mammogram Using Deep Convolutional Neural Network

Breast cancer is the most common cancer in women worldwide, and the mammogram is the most widely used screening technique for breast cancer. To make a diagnosis in the early stage of breast cancer, the appearance of masses and microcalcifications on the mammogram are two crucial indicators. Notably, the early detection of malignant microcalcifications can facilitate the diagnosis and the treatment of breast cancer at the appropriate time. Making an accurate evaluation on microcalcifications is a timeconsuming and challenging task for the radiologists due to the small size and the low contrast of microcalcification. Compared to the background and mammogram image with noises, it is tough to be discriminated. Computer-Aided Detection (CADe) have been deployed to support radiologists. However, most of current CADe systems need to have hand-crafted image features to be implemented. For improvement in the conventional approach, Convolutional Neural Network (CNN) with no hand-crafted image feature is used in this thesis. CNN with Class Activation Map (CAM) is deployed to implement the microcalcification detection in mammograms. GoogLeNet architecture with nine inception modules and one CAM layer is used to improve the localization capability of GoogLeNet in microcalcification detection while maintaining the local information. The network is trained and tested with Curated Breast Imaging Subset of Digital Database for Screening Mammography dataset (CBIS-DDSM). This approach demonstrates that the localization ability of CAM for abnormal microcalcification regions on the mammogram can be improved by restoring the last two inception modules that were removed in the paper [1] [16]. For the CAM, CAM layer is inserted in the position of the second auxiliary layer that was used in the original GoogLeNet [17] for training. This allowed us to use the intermediate feature at the same location from [1] [16] for localization while maintaining the depth of the GoogLeNet [17]. The experimental result shows that this method achieved about 225.15% better result at localizing microcalcification in mammograms than the existing method

Deep learning approach for breast cancer diagnosis

Breast cancer is one of the leading fatal disease worldwide with high risk control if early discovered. Conventional method for breast screening is x-ray mammography, which is known to be challenging for early detection of cancer lesions. The dense breast structure produced due to the compression process during imaging lead to difficulties to recognize small size abnormalities. Also, inter- and intra-variations of breast tissues lead to significant difficulties to achieve high diagnosis accuracy using hand-crafted features. Deep learning is an emerging machine learning technology that requires a relatively high computation power. Yet, it proved to be very effective in several difficult tasks that requires decision making at the level of human intelligence. In this paper, we develop a new network architecture inspired by the U-net structure that can be used for effective and early detection of breast cancer. Results indicate a high rate of sensitivity and specificity that indicate potential usefulness of the proposed approach in clinical use

Studies on deep learning approach in breast lesions detection and cancer diagnosis in mammograms

Breast cancer accounts for the largest proportion of newly diagnosed cancers in women recently. Early diagnosis of breast cancer can improve treatment outcomes and reduce mortality. Mammography is convenient and reliable, which is the most commonly used method for breast cancer screening. However, manual examinations are limited by the cost and experience of radiologists, which introduce a high false positive rate and false examination. Therefore, a high-performance computer-aided diagnosis (CAD) system is significant for lesions detection and cancer diagnosis. Traditional CADs for cancer diagnosis require a large number of features selected manually and remain a high false positive rate. The methods based on deep learning can automatically extract image features through the network, but their performance is limited by the problems of multicenter data biases, the complexity of lesion features, and the high cost of annotations. Therefore, it is necessary to propose a CAD system to improve the ability of lesion detection and cancer diagnosis, which is optimized for the above problems. This thesis aims to utilize deep learning methods to improve the CADs' performance and effectiveness of lesion detection and cancer diagnosis. Starting from the detection of multi-type lesions using deep learning methods based on full consideration of characteristics of mammography, this thesis explores the detection method of microcalcification based on multiscale feature fusion and the detection method of mass based on multi-view enhancing. Then, a classification method based on multi-instance learning is developed, which integrates the detection results from the above methods, to realize the precise lesions detection and cancer diagnosis in mammography. For the detection of microcalcification, a microcalcification detection network named MCDNet is proposed to overcome the problems of multicenter data biases, the low resolution of network inputs, and scale differences between microcalcifications. In MCDNet, Adaptive Image Adjustment mitigates the impact of multicenter biases and maximizes the input effective pixels. Then, the proposed pyramid network with shortcut connections ensures that the feature maps for detection contain more precise localization and classification information about multiscale objects. In the structure, trainable Weighted Feature Fusion is proposed to improve the detection performance of both scale objects by learning the contribution of feature maps in different stages. The experiments show that MCDNet outperforms other methods on robustness and precision. In case the average number of false positives per image is 1, the recall rates of benign and malignant microcalcification are 96.8% and 98.9%, respectively. MCDNet can effectively help radiologists detect microcalcifications in clinical applications. For the detection of breast masses, a weakly supervised multi-view enhancing mass detection network named MVMDNet is proposed to solve the lack of lesion-level labels. MVMDNet can be trained on the image-level labeled dataset and extract the extra localization information by exploring the geometric relation between multi-view mammograms. In Multi-view Enhancing, Spatial Correlation Attention is proposed to extract correspondent location information between different views while Sigmoid Weighted Fusion module fuse diagnostic and auxiliary features to improve the precision of localization. CAM-based Detection module is proposed to provide detections for mass through the classification labels. The results of experiments on both in-house dataset and public dataset, [email protected] and [email protected] (recall rate@average number of false positive per image), demonstrate MVMDNet achieves state-of-art performances among weakly supervised methods and has robust generalization ability to alleviate the multicenter biases. In the study of cancer diagnosis, a breast cancer classification network named CancerDNet based on Multi-instance Learning is proposed. CancerDNet successfully solves the problem that the features of lesions are complex in whole image classification utilizing the lesion detection results from the previous chapters. Whole Case Bag Learning is proposed to combined the features extracted from four-view, which works like a radiologist to realize the classification of each case. Low-capacity Instance Learning and High-capacity Instance Learning successfully integrate the detections of multi-type lesions into the CancerDNet, so that the model can fully consider lesions with complex features in the classification task. CancerDNet achieves the AUC of 0.907 and AUC of 0.925 on the in-house and the public datasets, respectively, which is better than current methods. The results show that CancerDNet achieves a high-performance cancer diagnosis. In the works of the above three parts, this thesis fully considers the characteristics of mammograms and proposes methods based on deep learning for lesions detection and cancer diagnosis. The results of experiments on in-house and public datasets show that the methods proposed in this thesis achieve the state-of-the-art in the microcalcifications detection, masses detection, and the case-level classification of cancer and have a strong ability of multicenter generalization. The results also prove that the methods proposed in this thesis can effectively assist radiologists in making the diagnosis while saving labor costs

Deep-Learning-Based Computer- Aided Systems for Breast Cancer Imaging: A Critical Review

[EN] 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/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010-January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. 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