Embracing Large Natural Data: Enhancing Medical Image Analysis via Cross-Domain Fine-Tuning
文献类型：期刊论文
| 作者 | Li, Qiankun1,2; Huang, Xiaolong3; Fang, Bo4; Chen, Huabao5; Ding, Siyuan6; Liu, Xu6 |
| 刊名 | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
 |
| 出版日期 | 2024-08-01 |
| 卷号 | 28 |
| 关键词 | Large natural data medical image cross-domain learning staged fine-tuning Large natural data medical image cross-domain learning staged fine-tuning |
| ISSN号 | 2168-2194 |
| DOI | 10.1109/JBHI.2023.3343518 |
| 通讯作者 | Liu, Xu(liuxu.north@outlook.com) |
| 英文摘要 | 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. |
| WOS研究方向 | Computer Science ; Mathematical & Computational Biology ; Medical Informatics |
| 语种 | 英语 |
| WOS记录号 | WOS:001301988500013 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/135161]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Liu, Xu |
| 作者单位 | 1.Univ Sci & Technol China, Dept Automat, Hefei 230027, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei 230031, Peoples R China 3.Chongqing Univ Technol, Sch Artificial Intelligence, Chongqing 401120, Peoples R China 4.Northeastern Univ, Coll Med & Biol Informat Engn, Shenyang 110004, Peoples R China 5.Hohai Univ, Coll Energy & Elect Engn, Nanjing 211100, Peoples R China 6.Gen Hosp Northern Theater Command, Dept Gastroenterol, Shenyang 110840, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Qiankun,Huang, Xiaolong,Fang, Bo,et al. Embracing Large Natural Data: Enhancing Medical Image Analysis via Cross-Domain Fine-Tuning[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2024,28. |
| APA | Li, Qiankun,Huang, Xiaolong,Fang, Bo,Chen, Huabao,Ding, Siyuan,&Liu, Xu.(2024).Embracing Large Natural Data: Enhancing Medical Image Analysis via Cross-Domain Fine-Tuning.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,28. |
| MLA | Li, Qiankun,et al."Embracing Large Natural Data: Enhancing Medical Image Analysis via Cross-Domain Fine-Tuning".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 28(2024). |
入库方式： OAI收割
来源：合肥物质科学研究院
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