Medical Image Super-Resolution via Diagnosis-Guided Attention

Medical image super-resolution (SR) is an important medical image processing task and is often helpful for downstream medical analysis tasks. Most of the conventional SR methods tried to generate visually more convincing images whereas ignoring the following downstream tasks. In this paper, we take the Alzheimer’s disease diagnosis as the downstream task and propose a novel diagnosis-guided medical image SR network, which can make the SR and diagnosis be boosted by each other. The method contains two sub-networks, i.e., the SR network and the diagnosis network. To achieve better diagnosis performance, in the SR network, we apply the deformable convolution to capture the regions of interest (ROIs) with different and irregular sizes and shapes, which are important for diagnosis. Moreover, to integrate the two tasks, i.e., SR and diagnosis, more profoundly, we design a novel diagnosis-guided attention module, which makes the key regions for diagnosis can be reconstructed more clearly by the SR network. The extensive experiments on medical image data sets show that the proposed method often outperforms other state-of-the-art SR methods, which demonstrates its effectiveness. The codes of this paper are released in https://github.com/WJingwei/SRDA.