会议论文

Wang, SS

英文

Lecture Notes in Computer Science

0302-9743

2025

15241

114-123

15th International Workshop on Machine Learning in Medical Imaging

Lecture Notes in Computer Science

OCT 06, 2024

Marrakesh, MOROCCO

[Wu, Ruoyou;Li, Cheng;Yang, Jing;Fan, Wenxin;Wang, Shanshan] Chinese Acad Sci, Paul C Lauterbur Res Ctr Biomed Imaging, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China.^[Wu, Ruoyou;Liang, Yong] Pengcheng Lab, Shenzhen 518055, Peoples R China.^[Wu, Ruoyou;Fan, Wenxin] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.^[Cheng, Jian] Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China.^[Zou, Juan] Changsha Univ Sci & Technol, Sch Phys & Elect Sci, Changsha 410114, Peoples R China.

Xu, X Cui, Z Rekik, I Ouyang, X Sun, K

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

978-3-031-73283-6; 978-3-031-73284-3

10.1007/978-3-031-73284-3_12

National Natural Science Foundation of China [62222118, U22A2040]; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application [2022B1212010011]; Shenzhen Science and Technology Program [RCYX20210706092104034, JCYJ20220531100213029]; Youth Innovation Promotion Association CAS

本校为其他机构

Deep learning-based dMRI super-resolution methods can effectively enhance image resolution by leveraging the learning capabilities of neural networks on large datasets. However, these methods tend to learn a fixed scale mapping between low-resolution (LR) and high-resolution (HR) images, overlooking the need for radiologists to scale the images at arbitrary resolutions. Moreover, the pixel-wise loss in the image domain tends to generate over-smoothed results, losing fine textures and edge information. To address these issues, we propose a novel continuous super-resolution method for dMRI, call...