285 research outputs found

    Abnormality Detection in Mammography using Deep Convolutional Neural Networks

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    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

    The impact of Digital Breast Tomosynthesis on BIRADS categorization of mammographic non-mass findings

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    Introduction: Mammography is the most used breast screening tool and was proven to reduce breast-cancer-associated mortality. The estimated sensitivity of mammography varies between 77% and 95%; however, sensitivity could be 26% lower in dense breasts than in entirely fatty breasts. The ability to represent the complex 3D breast architecture and early changes in anatomical structures in a 2D view is the biggest challenge for mammography. In Digital Breast Tomosynthesis (DBT), tomographic images are reconstructed from multiple projections acquired from different angles. This technique allows the generation of 3D data, reduction of tissue overlap and allows better evaluation of masses, architectural distortion, and asymmetries compared with conventional two-dimensional mammographic images.Objective: To evaluate the impact of Digital Breast Tomosynthesis on BIRADS categorization of mammographic non-mass findings.Methods: Prospective cohort for 180 women with mammographic non-mass findings who presented to Alexandria University Radio diagnosis Department either for screening or diagnostic purposes between July 2019 and August 2020 with mean age 51.44 ± 10.67 . Digital breast tomosynthesis and ultrasound was done for all patients. Lesions were evaluated on DM; DBT alone then combined DBT & DM. Comparison of results according to changes in BIRADS, diagnostic performance using histopathology as gold standard.Results: 208 non-mass findings were detected by conventional mammography (104 asymmetry, 35 architectural distortion, 69 micro calcifications), Tomosynthesis reduced the BIRADS 3 count by 32%, upgraded the count of BIRADS 4 lesions by 11.4% while upgraded the BIRADS 2 by 18.9% with consequent improvement of sensitivity and specificity, PPV, NPV and accuracy to 96%, 95%, 94%,97%, and 95.6%.Conclusion: Combined FFDM and DBT improved the diagnostic performance in evaluation of non-mass findings and proper BIRADS categorization

    Breast tomosynthesis in practice

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