Medical Task Adaptation

Medical task adaptation focuses on tailoring pre-trained models, such as Vision Transformers (ViTs) and large language models (LLMs), to specific medical applications, improving their performance and efficiency on tasks like image segmentation and diagnosis. Current research emphasizes efficient adaptation techniques, including prompt tuning, hierarchical decoding, and one-stage training methods, often leveraging architectures like SAM and GMoE-Adapters to overcome data limitations and improve generalization across diverse medical modalities and languages. This work is crucial for advancing healthcare by enabling the deployment of accurate and reliable AI tools in clinical settings, particularly in resource-constrained environments or for less-studied diseases.