Lesion Classification

Lesion classification in medical imaging aims to automatically identify and categorize lesions (abnormal tissue regions) from various imaging modalities, improving diagnostic accuracy and efficiency. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and transformer-based architectures, often incorporating techniques like Siamese networks for lesion tracking and deformable attention for improved feature fusion across multiple views. These advancements are impacting clinical practice by enabling more comprehensive analysis of lesions, reducing inter-observer variability in scoring, and potentially improving the assessment of treatment response. The ultimate goal is to provide clinicians with faster, more accurate, and objective diagnostic support.