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

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

Tumor Prediction in Mammogram using Neural Network

Detecting micro calcifications - early breast cancer indicators 2013; is visually tough while recognizing malignant tumors is a highly complicated issue. Digital mammography ensures early breast cancer detection through digital mammograms locating suspicious areas with benign/- malignant micro calcifications. Early detection is vital in treatment and survival of breast cancer as there are no sure ways to prevent it. This paper presents a method of tumor prediction based on extracting features from mammogram using Gabor filter with Discrete cosine transform and classify the features using Neural Network

Što zapravo možemo vidjeti primjenom računalno potpomognute analize kod mamografije?

The main goal of this study was to compare the results of computer aided detection (CAD) analysis in screening mammography with the results independently obtained by two radiologists for the same samples and to determine the sensitivity and specificity of CAD for breast lesions. A total of 436 mammograms were analyzed with CAD. For each screening mammogram, the changes in breast tissue recognized by CAD were compared to the interpretations of two radiologists. The sensitivity and specificity of CAD for breast lesions were calculated using contingency table. The sensitivity of CAD for all lesions was 54% and specificity 16%. CAD sensitivity for suspicious lesions only was 86%. CAD sensitivity for microcalcifications was 100% and specificity 45%. CAD mainly ‘mistook’ glandular parenchyma, connective tissue and blood vessels for breast lesions, and blood vessel calcifications and axillary folds for microcalcifications. In this study, we confirmed CAD as an excellent tool for recognizing microcalcifications with 100% sensitivity. However, it should not be used as a stand-alone tool in breast screening mammography due to the high rate of false-positive results.Svrha ovoga istraživanja bila je usporediti rezultate računalno potpomognute analize (computer aided detection, CAD) u probirnoj mamografiji s rezultatima analize dva neovisna radiologa te utvrditi osjetljivost i specifičnost CAD-a za lezije u dojkama. Analizirali smo 436 mamograma pomoću CAD-a i usporedili rezultate s interpretacijom dva neovisna radiologa. Izračunali smo osjetljivost i specifičnost CAD-a za lezije u dojkama putem tablica kontingencije. Osjetljivost CAD-a za otkrivanje svih lezija u dojkama iznosila je 54%, a specifičnost 16%. Osjetljivost CAD-a za sumnjive lezije bila je 86%, a za mikrokalcifikacije 100% uz specifičnost od 45%. CAD je uglavnom pogrešno interpretirao žljezdani parenhim, vezivno tkivo i krvne žile kao tvorbe u dojkama, dok je kalcifikacije u krvim žilama i aksilarni nabor miješao s mikrokalcifikacijama. Ovom studijom smo potvrdili da je CAD izvrstan alat za otkrivanje mikrokalcifikacija s osjetljivošću od 100%. No, ipak se ne bi trebao rabiti kao jedina metoda u probirnoj mamografiji dojki uzimajući u obzir količinu lažno pozitivnih rezultata koja je prilično visoka

Computer aided detection in mammography

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Detection of breast pathologies in digital mammography images by thresholding and mathematical morphology

This paper proposes an algorithm for mass and micro-calcification detection by manual thresholding and prewitt detector. This algorithm has been tested using mammography images of different densities from multiple databases of a health clinic and images taken from the internet (40 images in total). The results are very accurate, allowing better detection of breast pathologies (mass and micro-calcification). Finally, the detection of breast pathologies was performed using as input a detection algorithm specially designed for this purpose. After segmentation by manual thresholding, morphological opening, morphological dilatation and Prewitt contour detection we have a demarcation of the masses and breast micro-calcification. The results obtained show the robustness of the proposed manual thresholding method. In order to evaluate the efficiency of our pathology detector, we compared our results with those in the literature and performed a qualitative evaluation with a rate of 98.04% for the detection of breast pathologies.  A radiologist from the health clinic evaluated the results and considers them acceptable to the CAD