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Anomaly classification in digital mammography based on multiple-instance learning

Anomaly classification in digital mammography based on multiple-instance learning

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Cancer tissues in mammography images exhibit abnormal regions; it is of great clinical importance to label a mammography image as having cancerous regions or not, perform the corresponding image segmentation. However, the detailed annotation of the cancer region is often an ambiguous and challenging task. The authors describe a fully automatic computer-aided detection and diagnosis (CAD) system to detect and classify breast cancer as malignant or benign, by using mammography and building on the multiple-instance learning (MIL) algorithms, which has been confirmed beneficial for radiologist decision sustenance. Traditional learning methods require great effort to annotate the training data by costly manual labelling and specialised computational models to detect these annotations during the test. The proposed CAD system simultaneously performs pixel-level segmentation (suspicious versus normal tissue) and image-level classification (benign versus malignant image). The set-up of the proposed system is in order: automatically segmented regions of interest (ROIs). Then, features derived from ROIs detected such as textural features and shape features are selected and extracted from each region and combined them to classify ROIs as ‘benign’ or ‘malignant’, by implementing MIL algorithms. Experimental results demonstrate the efficiency and robustness of the proposed CAD system compared with previous work in the literature.

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