Nodule Detection

Nodule detection, primarily focused on identifying cancerous lung nodules in medical images, aims to improve early diagnosis and treatment of lung cancer. Current research emphasizes developing robust and generalizable deep learning models, often employing architectures like U-Nets, YOLO, Vision Transformers, and diffusion models, to address challenges such as data heterogeneity across institutions and the need for efficient, accurate segmentation and classification. These advancements hold significant potential for improving diagnostic accuracy, reducing radiologist workload, and ultimately enhancing patient outcomes, particularly in resource-constrained settings. Furthermore, research is actively exploring methods to improve model explainability and reliability, and to leverage multimodal data (including electronic health records) for more comprehensive diagnoses.