Heterogeneous knowledge adaptation mechanisms
HealthGPT's innovative H-LoRA (Hierarchical Low-Rank Adaptation) technology addresses the key challenge of multimodal learning in healthcare. The technology contains 4 sets of pluggable low-rank adapters corresponding to different levels of visual feature processing, supporting adjustable rank dimension settings from r=64-256. By freezing the base model parameters and training only the adapter weights, it achieves efficient injection of specialized medical knowledge while ensuring the generalized capability of the model.
In the specific implementation, the comprehension task uses com_hlora_weights (r=64) and the generation task uses gen_hlora_weights (r=256). This design allows the model to maintain excellent performance with limited medical data, with a single task adapter requiring only about 500MB of additional parameters, and improving training efficiency by more than 8 times over full parameter fine-tuning.
This answer comes from the articleHealthGPT: A Medical Big Model to Support Medical Image Analysis and Diagnostic Q&AThe
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