Squeeze & Excitation joint with Combined Channel and Spatial Attention for Pathology Image Super-Resolution

Super-resolution (SR) techniques are pivotal in enhancing low-resolution images and crucial in medical diagnosis, where detail and clarity are paramount. Traditional pixel-loss-based SR methods, while adept at producing high-resolution (HR) images, often result in artifice content. This loss of information compromises both the visual experience and the accuracy of subsequent diagnoses. Addressing this, we have developed an innovative SR approach integrating a joint Squeeze and Excitation (SE) mechanism with a Combined Channel and Spatial Attention (CCSA) mechanism. The SE mechanism effectively recalibrates channel-wise feature responses, enhancing the representational capacity of the network. Meanwhile, the CCSA mechanism focuses on extracting spatial and channel-wise features, ensuring that critical high-frequency details are preserved. The dual approach significantly refines the quality of the images, maintaining essential details necessary for accurate medical diagnosis. To validate our proposed approach, we used a benchmark dataset, bcSR, tailored to challenge SR models to focus on broader and more critical regions. Comparative analysis proves that our model excels in performance over existing state-of-the-art methods. In conclusion, our proposed SR Network, with its innovative SE and CCSA mechanisms, offers a potent tool for pathology image SR. It elevates the quality of super-resolved images, which will significantly aid in the accuracy and efficiency of medical diagnoses, providing a valuable asset to medical professionals.