Lesion Synthesis

Lesion synthesis focuses on generating realistic synthetic medical images containing lesions to augment limited real-world datasets used in training deep learning models for medical image analysis. Current research employs generative adversarial networks (GANs), variational autoencoders (VAEs), and other advanced architectures, often incorporating strategies like partial convolutions or conditional embeddings to improve the realism and diversity of synthesized lesions. This approach addresses the critical need for large, annotated datasets in medical imaging, ultimately improving the accuracy and robustness of automated lesion detection and segmentation in applications such as brain tumor or multiple sclerosis diagnosis and treatment monitoring.