Anomaly classification in digital mammography based on multiple‐instance learning. Issue 3 (1st March 2018)

• Record Type: Journal Article
• Authors: Elmoufidi, Abdelali; El Fahssi, Khalid; Jai‐andaloussi, Said; Sekkaki, Abderrahim; Gwenole, Quellec; Lamard, Mathieu
• Is Part Of: IET image processing. Volume 12:Issue 3(2018)
• Page Start: 320
• Page End: 328
• Publication Date: 2018-03-01
• Subjects: mammography -- medical image processing -- image segmentation -- learning (artificial intelligence) -- feature extraction -- cancer -- image classification -- feature selection; Mammography Image Analysis Society database -- Digital Database for Screening Mammography -- feature selection -- shape feature extraction -- textural feature extraction -- image‐level classification -- pixel‐level segmentation -- MIL algorithms -- CAD system -- fully automatic computer‐aided detection and diagnosis system -- image segmentation -- cancer tissues -- multiple‐instance learning -- digital mammography -- anomaly classification; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0536
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.