Tissue Phenotyping

Tissue phenotyping uses computational methods to analyze tissue images, aiming to objectively characterize tissue structures and biomarkers for improved disease diagnosis and treatment. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks, graph neural networks, and vision transformers, often combined with techniques such as self-supervised learning and spectral clustering to overcome challenges posed by large datasets and limited annotations. These advancements are improving the accuracy and efficiency of tasks such as lesion segmentation, biomarker identification, and disease subtyping across various imaging modalities (e.g., ultrasound, MRI, histology), with significant implications for pathology, radiology, and personalized medicine.