AEBSR: Active-Sampling and Energy-Based Single Image Super-Resolution

Object detection, a crucial component of medical image analysis, provides physicians with an interpretable auxiliary diagnostic basis. Although existing object detection models have had great success with natural images, the growing resolution of medical images makes the problem especially challenging because of the increased expectations to exploit the image details and discover small targets in images. For instance, lesions are occasionally diminutive relative to high-resolution medical images. To address this problem, we present YOLO-SG, a salience-guided (SG) deep learning model that improves small object detection by attending to detailed regions via a generated salience map. YOLO-SG performs two rounds of detection: coarse detection and salience-guided detection. in the first round of coarse detection, YOLO-SG detects objects using a deep convolutional detection model and proposes a salience map utilizing the context surrounding objects to guide the subsequent round of detection. in the second round, YOLO-SG extracts salient regions from the original input image based on the generated salience map and combines local detail with global context information to improve the object detection performance. The experimental results demonstrate that YOLO-SG outperforms the state-of-the-art models, especially when detecting small objects.