New training method helps AI models handle messy, varied medical image data

MIT neuroscientists have discovered a surprising similarity in the problem-solving approaches of humans and new artificial intelligence models. This research highlights the cognitive processes involved in complex decision-making and could have implications for the development of AI systems.

LINGUAL is a new framework designed to enhance active learning in medical image segmentation by utilizing natural language instructions from experts. This approach aims to reduce the cognitive load associated with precise boundary delineation in segmentation tasks, which can be labor-intensive and challenging. By translating language guidance into executable programs, LINGUAL allows for more efficient annotation of regions of interest (ROIs) in medical images, potentially lowering costs and improving accuracy in medical imaging.

SAM-Fed is a proposed framework for federated semi-supervised learning (FSSL) aimed at improving medical image segmentation. It addresses challenges such as data privacy and the high cost of expert annotation, which limit the availability of labeled data. SAM-Fed utilizes a high-capacity segmentation foundation model to guide lightweight client devices during training, combining dual knowledge distillation with an adaptive agreement mechanism to enhance the reliability of pseudo-labels in segmentation tasks.