Detecting and classifying lesions in mammograms with Deep Learning

In the last two decades Computer Aided Diagnostics (CAD) systems were developed to help radiologists analyze screening mammograms. The benefits of current CAD technologies appear to be contradictory and they should be improved to be ultimately considered useful. Since 2012 deep convolutional neural networks (CNN) have been a tremendous success in image recognition, reaching human performance. These methods have greatly surpassed the traditional approaches, which are similar to currently used CAD solutions. Deep CNN-s have the potential to revolutionize medical image analysis. We propose a CAD system based on one of the most successful object detection frameworks, Faster R-CNN. The system detects and classifies malignant or benign lesions on a mammogram without any human intervention. The proposed method sets the state of the art classification performance on the public INbreast database, AUC = 0.95 . The approach described here has achieved the 2nd place in the Digital Mammography DREAM Challenge with AUC = 0.85 . When used as a detector, the system reaches high sensitivity with very few false positive marks per image on the INbreast dataset. Source code, the trained model and an OsiriX plugin are availaible online at https://github.com/riblidezso/frcnn_cad

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

Digital mammography, cancer screening: Factors important for image compression

The use of digital mammography for breast cancer screening poses several novel problems such as development of digital sensors, computer assisted diagnosis (CAD) methods for image noise suppression, enhancement, and pattern recognition, compression algorithms for image storage, transmission, and remote diagnosis. X-ray digital mammography using novel direct digital detection schemes or film digitizers results in large data sets and, therefore, image compression methods will play a significant role in the image processing and analysis by CAD techniques. In view of the extensive compression required, the relative merit of 'virtually lossless' versus lossy methods should be determined. A brief overview is presented here of the developments of digital sensors, CAD, and compression methods currently proposed and tested for mammography. The objective of the NCI/NASA Working Group on Digital Mammography is to stimulate the interest of the image processing and compression scientific community for this medical application and identify possible dual use technologies within the NASA centers

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Use of computer-aided detection (CAD) tools in screening mammography: a multidisciplinary investigation

We summarise a set of analyses and studies conducted to assess the effects of the use of a computer-aided detection (CAD) tool in breast screening. We have used an interdisciplinary approach that combines: (a) statistical analyses inspired by reliability modelling in engineering; (b) experimental studies of decisions of mammography experts using the tool, interpreted in the light of human factors psychology; and (c) ethnographic observations of the use of the tool both in trial conditions and in everyday screening practice. Our investigations have shown patterns of human behaviour and effects of computer-based advice that would not have been revealed by a standard clinical trial approach. For example, we found that the negligible measured effect of CAD could be explained by a range of effects on experts' decisions, beneficial in some cases and detrimental in others. There is some evidence of the latter effects being due to the experts using the computer tool differently from the intentions of the developers. We integrate insights from the different pieces of evidence and highlight their implications for the design, evaluation and deployment of this sort of computer tool

Computer aided monitoring of breast abnormalities in X-ray mammograms

X­ray mammography is regarded as the most effective tool for the detection and diagnosis of breast cancer, but the interpretation of mammograms is a difficult and error­prone task. Computer­aided detection (CADe) systems address the problem that radiologists often miss signs of cancers that are retrospectively visible in mammograms. Furthermore, computer­aided diagnosis (CADx) systems assist the radiologist in the classification of mammographic lesions as benign or malignant[1]. This paper details a novel alternative system namely computer­aided monitoring (CAM) system. The designed CAM system can be used to objectively measure the properties of a suspected abnormal area in a mammogram. Thus it can be used to assist the clinician to objectively monitor the abnormality. For instance its response to treatment and consequently its prognosis. The designed CAM system is implemented using the Hierarchical Clustering based Segmentation (HCS) [2] [3] [4] process. Brief description of the implementation of this CAM system is as follows : Using the approximate location and size of the abnormality, obtained from the user, the HCS process automatically identifies the more appropriate boundaries of the different regions within a region of interest (ROI), centred at the approximate location. From the set of, HCS process segmented, regions the user identifies the regions which most likely represent the abnormality and the healthy areas. Subsequently the CAM system compares the characteristics of the user identified abnormal region with that of the healthy region; to differentiate malignant from benign abnormality. In processing sixteen mammograms from mini­MIAS [5], the designed CAM system demonstrated a success rate of 100% in differentiating malignant from benign abnormalities

Computer aids and human second reading as interventions in screening mammography: two systematic reviews to compare effects on cancer detection and recall rate

Background: There are two competing methods for improving the accuracy of a radiologist interpreting screening mammograms: computer aids (CAD) or independent second reading. Methods: Bibliographic databases were searched for clinical trials. Meta-analyses estimated impacts of CAD and double reading on odds ratios for cancer detection and recall rates. Sub-group analyses considered double reading with arbitration. Results: Ten studies compared single reading with CAD to single reading. Seventeen compared double to single reading. Double reading increases cancer detection and recall rates. Double reading with arbitration increases detection rate (CI: 1.02-1.15) and decreases recall rate (CI: 0.92-0.96). CAD does not have a significant effect on cancer detection rate (CI: 0.96-1.13) and increases recall rate (95% CI: 1.09-1.12). However, there is considerable heterogeneity in the impact on recall rate in both sets of studies. Conclusion: The evidence that double reading with arbitration enhances screening is stronger than that for single reading with CAD