Attention mechanisms for fine-grained biomedical image analysis - Fan, M (2020)
Problem: Recently, deep convolutional neural networks have greatly improved our ability to develop robust image analysis methods, especially those tailored to biomedical scenarios. Despite important advances in computer vision, it is usually challenging to achieve the same success on specific biomedical datasets due to certain specific challenges, such as limited size datasets, expensive annotations, high-resolution information, multiple discernible biomedical objects, and lack of interpretability. Therefore, it is still in high demand to investigate automatic image analysis methods with high interpretability and accuracy to transform high-resolution biomedical image data into meaningful quantitative information with weak annotations and limited availability of training data.