| Résumé | Breast cancer is the most common cancer in women worldwide. Computer Aided Detection (CADe) has attracted increasing research interest in recent years. Data exploration and lesion detection in medical images are tedious but can be accelerated using computational intelligence. One approach is to adapt and configure recent deep learning-based object detectors from computer vision to detect abnormalities in medical images. This chapter starts with three state-of-the-art object detectors, namely Faster R-CNN, YOLO v2 and Grad-CAM, to determine the location of lesions in Magnetic Resonance Images (MRI) of breast. Each individual detector is first tuned through adjusting network hyper-parameters and backbone architectures, in order to maximize Average Precision (AP). Two different integration approaches, namely cascaded and parallel integrations, are then proposed and implemented to improve the AP. The cascaded integration builds on a coarse-to-fine strategy. It uses Grad-CAM to compute coarse locations of bounding boxes for lesions and then applies YOLO v2 or Faster R-CNN as a fine detector. In the parallel integration approach, the detection result is a combination of results from the three detectors, with the aim of reducing missed detections. The integrated deep learning models exhibit enhanced performance on the detection of breast lesions in MRIs. |
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