Accelerating Deep Learning Medical Image Analysis in Radiology
Radiology, one of the cornerstones of modern healthcare, is undergoing rapid and profound changes due to the ever-increasing number of imaging examinations, the shortage of certified radiologists, the dynamics of healthcare economics, and the technological developments of artificial intelligence based image processing. This talk will present an overview of our new methods for the fast development of deep learning-based image processing solutions in Radiology with very few annotated datasets. The key idea is to bootstrap the creation of expert-validated annotations with new techniques for annotation uncertainty estimation and for learning how experts correct annotations generated by deep learning networks initially trained withvery few annotated datasets. Our methods aim to optimize radiologist time, reduce the annotated dataset size, and increase the accuracy and robustness of the deep neural networks results. We expect that our methods will significantly lower the entry cost, shorten the time and reduce the effort currently required to develop and deploy deep learning based solutions for radiology.
Published on: April 28, 2022
doi: 10.17756/micr.2022-suppl1
Citation: Proceedings of the Third International Conference on Medical Imaging and Case Reports (MICR-2022). J Med Imaging Case Rep 6(1): S1-S10.
