Multi Magnification

Multi-magnification techniques in digital pathology aim to leverage the information contained across different magnification levels within whole slide images (WSIs) for improved diagnostic accuracy. Current research focuses on developing novel model architectures, such as graph-structured pyramidal representations and vision transformers, that effectively integrate multi-magnification data, often employing self-supervised or weakly-supervised learning methods to address the scarcity of fully annotated WSIs. These advancements hold significant promise for improving the efficiency and accuracy of cancer subtyping and detection, potentially leading to more reliable and faster diagnostic tools in clinical settings.