Medical Image Compression

Medical image compression aims to reduce the storage and transmission costs of large medical datasets while preserving diagnostic information. Current research focuses on developing novel compression algorithms, including those based on implicit neural representations, fractal segmentation, and transfer learning, often incorporating techniques like wavelet transforms and knowledge distillation to improve efficiency and speed. These advancements are crucial for enabling remote diagnosis, facilitating data sharing for collaborative research, and improving the scalability of AI-driven medical applications. The ultimate goal is to optimize compression for specific diagnostic tasks, minimizing data loss while maximizing the utility of the compressed images for clinical use.