Embracing Large Natural Data: Enhancing Medical Image Analysis via Cross-Domain Fine-Tuning.
IEEE J Biomed Health Inform
; 28(8): 4512-4521, 2024 Aug.
Article
en En
| MEDLINE
| ID: mdl-38100336
ABSTRACT
With the rapid advancements of Big Data and computer vision, many large-scale natural visual datasets are proposed, such as ImageNet-21K, LAION-400M, and LAION-2B. These large-scale datasets significantly improve the robustness and accuracy of models in the natural vision domain. However, the field of medical images continues to face limitations due to relatively small-scale datasets. In this article, we propose a novel method to enhance medical image analysis across domains by leveraging pre-trained models on large natural datasets. Specifically, a Cross-Domain Transfer Module (CDTM) is proposed to transfer natural vision domain features to the medical image domain, facilitating efficient fine-tuning of models pre-trained on large datasets. In addition, we design a Staged Fine-Tuning (SFT) strategy in conjunction with CDTM to further improve the model performance. Experimental results demonstrate that our method achieves state-of-the-art performance on multiple medical image datasets through efficient fine-tuning of models pre-trained on large natural datasets.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Bases de Datos Factuales
Límite:
Humans
Idioma:
En
Revista:
IEEE J Biomed Health Inform
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Estados Unidos