Anomaly classification in digital mammography based on multiple‐instance learning
| Autor: | Abderrahim Sekkaki, Said Jai-Andaloussi, Mathieu Lamard, Khalid El Fahssi, Abdelali Elmoufidi, Quellec Gwenole |
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| Rok vydání: | 2018 |
| Předmět: |
Digital mammography
Computer science Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION CAD Feature selection 02 engineering and technology 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering medicine Mammography Segmentation Electrical and Electronic Engineering medicine.diagnostic_test Contextual image classification business.industry Pattern recognition Image segmentation ComputingMethodologies_PATTERNRECOGNITION Signal Processing 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Software |
| Zdroj: | IET Image Processing. 12:320-328 |
| ISSN: | 1751-9667 |
| DOI: | 10.1049/iet-ipr.2017.0536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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