Salient Patch

Salient patch identification focuses on selecting the most informative regions within an image for improved efficiency and interpretability in various applications. Current research employs methods like masked autoencoders and transformers, often within a hierarchical or iterative framework, to identify these patches, leveraging both supervised and unsupervised learning approaches. This technique is proving valuable in diverse fields, enhancing data efficiency in reinforcement learning, enabling processing of extremely high-resolution images with limited computational resources, and improving the interpretability of deep learning models in medical image analysis. The resulting improvements in efficiency and explainability are significant advancements for both computational and medical imaging.