HealthGPT: A Medical Large Vision-Language Model for Unifying
  Comprehension and Generation via Heterogeneous Knowledge Adaptation
We present HealthGPT, a powerful Medical Large Vision-Language Model
(Med-LVLM) that integrates medical visual comprehension and generation
capabilities within a unified autoregressive paradigm. Our bootstrapping
philosophy is to progressively adapt heterogeneous comprehension and generation
knowledge to pre-trained large language models (LLMs). This is achieved through
a novel heterogeneous low-rank adaptation (H-LoRA) technique, which is
complemented by a tailored hierarchical visual perception approach and a
three-stage learning strategy. To effectively learn the HealthGPT, we devise a
comprehensive medical domain-specific comprehension and generation dataset
called VL-Health. Experimental results demonstrate exceptional performance and
scalability of HealthGPT in medical visual unified tasks. Our project can be
accessed at https://github.com/DCDmllm/HealthGPT.
67b55078a64445f58c771d84
Tianwei Lin
67b55078a64445f58c771d85
Wenqiao Zhang
admin_assigned
2025-02-19T14:40:41.324Z
65fc18edfb66882aba4d548e
/avatars/f70d47fe4aba98b5a5cd64f7e002dfd2.svg
wenqiao
user
wannature
67b55078a64445f58c771d86
Sijing Li
67b55078a64445f58c771d87
Yuqian Yuan
67b55078a64445f58c771d88
Binhe Yu
admin_assigned
2025-02-19T14:41:15.907Z
648ad056dc6a5b4b88306ae2
/avatars/66a47a11626c05b706de33a3184182e9.svg
yu
user
binheyu1991
67b55078a64445f58c771d89
Haoyuan Li
67b55078a64445f58c771d8a
Wanggui He
67b55078a64445f58c771d8b
Hao Jiang
67b55078a64445f58c771d8c
Mengze Li
67b55078a64445f58c771d8d
Xiaohui Song
admin_assigned
2025-02-19T14:42:09.080Z
635156f1f74cdcca6f7acf70
/avatars/133e88b30255425f6da7777737f91e81.svg
Xiaohui Song
user
fpcsong
67b55078a64445f58c771d8e
Siliang Tang
67b55078a64445f58c771d8f
Jun Xiao
67b55078a64445f58c771d90
Hui Lin
67b55078a64445f58c771d91
Yueting Zhuang
67b55078a64445f58c771d92
Beng Chin Ooi
HealthGPT: A Medical Large Vision-Language Model for Unifying
  Comprehension and Generation via Heterogeneous Knowledge Adaptation
We present HealthGPT, a powerful Medical Large Vision-Language Model
(Med-LVLM) that integrates medical visual comprehension and generation
capabilities within a unified autoregressive paradigm. Our bootstrapping
philosophy is to progressively adapt heterogeneous comprehension and generation
knowledge to pre-trained large language models (LLMs). This is achieved through
a novel heterogeneous low-rank adaptation (H-LoRA) technique, which is
complemented by a tailored hierarchical visual perception approach and a
three-stage learning strategy. To effectively learn the HealthGPT, we devise a
comprehensive medical domain-specific comprehension and generation dataset
called VL-Health. Experimental results demonstrate exceptional performance and
scalability of HealthGPT in medical visual unified tasks. Our project can be
accessed at https://github.com/DCDmllm/HealthGPT.
HealthGPT, a Medical Large Vision-Language Model, combines visual comprehension and generation using heterogeneous low-rank adaptation and hierarchical visual perception.
Medical Large Vision-Language Model Med-LVLM autoregressive paradigm heterogeneous low-rank adaptation H-LoRA hierarchical visual perception VL-Health