A Computer Aided Detection system for mammographic images implemented on a GRID infrastructure

The use of an automatic system for the analysis of mammographic images has proven to be very useful to radiologists in the investigation of breast cancer, especially in the framework of mammographic-screening programs. A breast neoplasia is often marked by the presence of microcalcification clusters and massive lesions in the mammogram: hence the need for tools able to recognize such lesions at an early stage. In the framework of the GPCALMA (GRID Platform for Computer Assisted Library for MAmmography) project, the co-working of italian physicists and radiologists built a large distributed database of digitized mammographic images (about 5500 images corresponding to 1650 patients) and developed a CAD (Computer Aided Detection) system, able to make an automatic search of massive lesions and microcalcification clusters. The CAD is implemented in the GPCALMA integrated station, which can be used also for digitization, as archive and to perform statistical analyses. Some GPCALMA integrated stations have already been implemented and are currently on clinical trial in some italian hospitals. The emerging GRID technology can been used to connect the GPCALMA integrated stations operating in different medical centers. The GRID approach will support an effective tele- and co-working between radiologists, cancer specialists and epidemiology experts by allowing remote image analysis and interactive online diagnosis.Comment: 5 pages, 5 figures, to appear in the Proceedings of the 13th IEEE-NPSS Real Time Conference 2003, Montreal, Canada, May 18-23 200

Healthcare technologies and professional vision

This paper presents some details from an observational evaluation of a computer assisted detection tool in mammography. The use of the tool, its strengths and weaknesses, are documented and its impact on reader's 'professional vision' (Goodwin 1994) considered. The paper suggests issues for the design, use and, importantly, evaluation of new technologies in everyday medical work, pointing to general issues concerning trust – users’ perception of the dependability of the evidence generated by such tools and suggesting that evaluations require an emphasis on the complex issue of what technologies afford their users in everyday work

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

Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks

Breast cancer is one of the most frequent causes of mortality in women. For the early detection of breast cancer, the mammography is used as the most efficient technique to identify abnormalities such as tumors. Automatic detection of tumors in mammograms has become a big challenge and can play a crucial role to assist doctors in order to achieve an accurate diagnosis. State-of-the-art Deep Learning algorithms such as Faster Regional Convolutional Neural Networks are able to determine the presence of an object and also its position inside the image in a reduced computation time. In this work, we evaluate these algorithms to detect tumors in mammogram images and propose a detection system that contains: (1) a preprocessing step performed on mammograms taken from the Digital Database for Screening Mammography (DDSM) and (2) the Neural Network model, which performs feature extraction over the mammograms in order to locate tumors within each image and classify them as malignant or benign. The results obtained show that the proposed algorithm has an accuracy of 97.375%. These results show that the system could be very useful for aiding physicians when detecting tumors from mammogram images.Ministerio de Economía y Competitividad TEC2016-77785-

Can high-frequency ultrasound predict metastatic lymph nodes in patients with invasive breast cancer?

Aim To determine whether high-frequency ultrasound can predict the presence of metastatic axillary lymph nodes, with a high specificity and positive predictive value, in patients with invasive breast cancer. The clinical aim is to identify patients with axillary disease requiring surgery who would not normally, on clinical grounds, have an axillary dissection, so potentially improving outcome and survival rates. Materials and methods The ipsilateral and contralateral axillae of 42 consecutive patients with invasive breast cancer were scanned prior to treatment using a B-mode frequency of 13 MHz and a Power Doppler frequency of 7 MHz. The presence or absence of an echogenic centre for each lymph node detected was recorded, and measurements were also taken to determine the L/S ratio and the widest and narrowest part of the cortex. Power Doppler was also used to determine vascularity. The contralateral axilla was used as a control for each patient. Results In this study of patients with invasive breast cancer, ipsilateral lymph nodes with a cortical bulge ≥3 mm and/or at least two lymph nodes with absent echogenic centres indicated the presence of metastatic axillary lymph nodes (10 patients). The sensitivity and specificity were 52.6% and 100%, respectively, positive and negative predictive values were 100% and 71.9%, respectively, the P value was 0.001 and the Kappa score was 0.55.\ud Conclusion This would indicate that high-frequency ultrasound can be used to accurately predict metastatic lymph nodes in a proportion of patients with invasive breast cancer, which may alter patient management