Deep Learning Applications in Medical Imaging

Dear Colleagues,

The success of deep learning (DL) and pattern recognition in many medical image analysis applications has suggested that DL, or artificial intelligence (AI), can bring revolutionary changes in healthcare. Instead of subjective experience-driven diagnosis and prognosis, the widespread accumulation of medical data offers unprecedented opportunities for data-driven DL algorithms to learn about accurate and robust models. Despite the optimism in this new era of DL, the development and implementation of DL or AI tools in clinical practice face many challenges, including the segmentation of small lesions, a performance drop in different scanners and populations, costly labeling, data privacy concerns, as well as the reliability and comprehensibility of AI models.

The aim of this Special Issue is to highlight the advances and technologies in deep learning and pattern recognition in medical image analysis, thereby helping to provide reliable intelligent aids for patient care. The topics of interest include, but are not limited to, the following:

1. disease diagnosis and prognosis;
2. medical image reconstruction;
3. medical image processing;
4. lesion or anatomical structure segmentation;
5. uncertainty quantification in medical image analysis;
6. transfer learning for cross modality, scanners, and population;
7. image registration and atlas construction;
8. explainable medial AI systems;
9. privacy in medical data analysis;
10. multimodality medical data analysis;
11. novel public clinical datasets with baselines.

Dr. Xiaofeng Liu
Guest Editor

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