16 research outputs found
An Automatic System to Discriminate Malignant from Benign Massive Lesions on Mammograms
Mammography is widely recognized as the most reliable technique for early
detection of breast cancers. Automated or semi-automated computerized
classification schemes can be very useful in assisting radiologists with a
second opinion about the visual diagnosis of breast lesions, thus leading to a
reduction in the number of unnecessary biopsies. We present a computer-aided
diagnosis (CADi) system for the characterization of massive lesions in
mammograms, whose aim is to distinguish malignant from benign masses. The CADi
system we realized is based on a three-stage algorithm: a) a segmentation
technique extracts the contours of the massive lesion from the image; b)
sixteen features based on size and shape of the lesion are computed; c) a
neural classifier merges the features into an estimated likelihood of
malignancy. A dataset of 226 massive lesions (109 malignant and 117 benign) has
been used in this study. The system performances have been evaluated terms of
the receiver-operating characteristic (ROC) analysis, obtaining A_z =
0.80+-0.04 as the estimated area under the ROC curve.Comment: 6 pages, 3 figures; Proceedings of the ITBS 2005, 3rd International
Conference on Imaging Technologies in Biomedical Sciences, 25-28 September
2005, Milos Island, Greec
Breast Density Transformations Using CycleGANs for Revealing Undetected Findings in Mammograms
Breast cancer is the most common cancer in women, a leading cause of morbidity and mortality, and a significant health issue worldwide. According to the World Health Organization’s cancer awareness recommendations, mammographic screening should be regularly performed on middle-aged or older women to increase the chances of early cancer detection. Breast density is widely known to be related to the risk of cancer development. The American College of Radiology Breast Imaging Reporting and Data System categorizes mammography into four levels based on breast density, ranging from ACR-A (least dense) to ACR-D (most dense). Computer-aided diagnostic (CAD) systems can now detect suspicious regions in mammograms and identify abnormalities more quickly and accurately than human readers. However, their performance is still influenced by the tissue density level, which must be considered when designing such systems. In this paper, we propose a novel method that uses CycleGANs to transform suspicious regions of mammograms from ACR-B, -C, and -D levels to ACR-A level. This transformation aims to reduce the masking effect caused by thick tissue and separate cancerous regions from surrounding tissue. Our proposed system enhances the performance of conventional CNN-based classifiers significantly by focusing on regions of interest that would otherwise be misidentified due to fatty masking. Extensive testing on different types of mammograms (digital and scanned X-ray film) demonstrates the effectiveness of our system in identifying normal, benign, and malignant regions of interest
Breast Lesion Detection from Mammograms Using Deep Convolutional Neural Networks
4nononeMammography has a central role in screening and diagnosis of breast lesions, allowing early detection of the pathology and reduction of fatal cases. Deep Convolutional Neural Networks have shown a great potentiality to address the issue of early detection of breast cancer with an acceptable level of accuracy and reproducibility. In the present paper, we illustrate the development of a deep learning study aimed to process and classify lesions in mammograms with the use of slender neural networks not yet used in literature. For this reason, a traditional convolution network was compared with a novel one obtained making use of much more efficient depth wise separable convolution layers. Preliminary numerical results are detailed and future plans outlined.noneGloria Gonella, Marco Paracchini, Elisabetta Binaghi, Marco MarconGonella, Gloria; Paracchini, Marco; Binaghi, Elisabetta; Marcon, Marc
Refraction corrected transmission ultrasound computed tomography for application in breast imaging
Purpose: We present an iterative framework for CT reconstruction from transmission ultrasound data which accurately and efficiently models the strong refraction effects that occur in our target application: Imaging the female breast