Lesion Annotation

Lesion annotation in medical imaging focuses on efficiently and accurately labeling regions of interest (lesions) within medical scans, crucial for training and evaluating diagnostic algorithms. Current research emphasizes reducing the need for extensive manual annotation through techniques like interactive segmentation tools guided by a few user clicks, self-supervised learning from unlabeled data, and leveraging information from accompanying clinical reports to generate pseudo-labels. These methods, often employing convolutional neural networks (CNNs) and transformers, aim to improve the accuracy and efficiency of lesion detection and segmentation across various imaging modalities and anatomical locations, ultimately enhancing the development and application of AI-powered diagnostic tools.