Recent advances and clinical applications of deep learning in medical image analysis
By Xuxin Chen, Xi-ming Wang, Kecheng Zhang, Roy Zhang, Kar-Ming Fung, Theresa C. Thai, K. Moore, Robert S. Mannel, Hong Liu, Bin Zheng and 1 more. Medical Image Anal., 2021
981 citations recorded by the source corpus.
Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis. Despite the success, the further improvement of deep learning models in medical image analysis is majorly bottlenecked by the lack of large-sized and well-annotated datasets. In the past five years, many studies have focused on addressing this challenge. In this paper, we reviewed and summarized these recent studies to provide a comprehensive overview of applying deep learning methods in various medical image analysis tasks. Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical image analysis, which are summarized based on different application scenarios, including classification, segmentation, detection, and image registration. We also discuss the major technical challenges and suggest the possible solutions in future research efforts.
Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/968b7f11bf90fbbf18167bca94d86beeac671548
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In Artificial Intelligence
- Alan L. Yuille
- Zongwei Zhou
- Evgin Göçeri
- Yixuan Yuan
- Yucheng Tang
- Jieneng Chen
- Quoc V. Le
- Trevor Darrell
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Medical image data augmentation: techniques, comparisons and interpretations (2023, methodology): “(iii) with a specific technique, such as Generative Adversarial Network (GAN) (Chen et al. 2022b), erasing and mixing (Naveed 2021).”
- CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection (2023, background): “Although a total of around 5,000 annotated abdominal CT scans are publicly available, an ingrained impression remains: medical imaging datasets are too small to develop robust AI models [13,45,55,61,78,79].”