Deep Learning in Medical Imaging: Application Progress and Future Challenges

Jingwen Wang; Pingping Yang; Ziyi An; Run Luo; Jiayi  Feng; Linghan Zhang

doi:10.54691/kskfkp57

Authors

Jingwen Wang
Pingping Yang
Ziyi An
Run Luo
Jiayi Feng
Linghan Zhang

DOI:

https://doi.org/10.54691/kskfkp57

Keywords:

Medical Imaging; Deep Learning; Image Reconstruction; Computer-aided diagnosis; Image Segmentation; Federated Learning; Multimodal Large Models.

Abstract

Medical Imaging occupies a central position in modern clinical diagnosis and treatment. However, faced with vast and continuously growing volumes of complex imaging data, traditional image processing algorithms reliant on manual features struggle to meet the demands for efficient and precise clinical applications. Against this backdrop, Deep Learning technology, leveraging its unique advantages, has demonstrated immense application potential in Medical Imaging, significantly enhancing the accuracy and efficiency of medical diagnosis. This paper systematically reviews the primary technological developments and practical application scenarios of Deep Learning within the Medical Imaging domain. The focus will be on key application areas such as image reconstruction, segmentation, and registration, alongside multimodal synthesis, intelligent lesion detection, and computer-aided diagnosis. Finally, the paper will delve into the current limitations of these technologies and explore future development directions, aiming to provide valuable reference for research and application in this field.

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