2021 International Conference on Medical Imaging Physics and Engineering and the 10th National Annual Meeting of Medical Imaging Physics (ICMIPE 2021)

 Artificial intelligence or deep learning technologies have gained prevalence in solving medical imaging tasks. In this talk, we first review the traits that characterize medical images, such as multi-modalities, heterogeneous and isolated data, sparse and noisy labels, imbalanced samples. We then point out the necessity of a paradigm shift from "small task, big data" to "big task, small data". Finally, we illustrate the trends of AI technologies in medical imaging and present a multitude of algorithms that attempt to address various aspects of “big task, small data”:

• Annotation-efficient methods that tackle medical image analysis without many labelled instances, including one-shot or label-free inference approaches.
• Universal models that learn “common + specific” feature representations for multi-domain tasks to unleash the potential of ‘bigger data’, which are formed by integrating multiple datasets associated with tasks of interest into one use.
• "Deep learning + knowledge modeling" approaches, which combine machine learning with domain knowledge to enable start-of-the-art performances for many tasks of medical image reconstruction, recognition, segmentation, and parsing.