An integrated iterative annotation technique for easing neural network training in medical image analysis. Nat Mach Intell 2019 Feb;1(2):112-119
Date
06/13/2019Pubmed ID
31187088Pubmed Central ID
PMC6557463DOI
10.1038/s42256-019-0018-3Scopus ID
2-s2.0-85074196829 (requires institutional sign-in at Scopus site) 89 CitationsAbstract
Neural networks promise to bring robust, quantitative analysis to medical fields. However, their adoption is limited by the technicalities of training these networks and the required volume and quality of human-generated annotations. To address this gap in the field of pathology, we have created an intuitive interface for data annotation and the display of neural network predictions within a commonly used digital pathology whole-slide viewer. This strategy used a 'human-in-the-loop' to reduce the annotation burden. We demonstrate that segmentation of human and mouse renal micro compartments is repeatedly improved when humans interact with automatically generated annotations throughout the training process. Finally, to show the adaptability of this technique to other medical imaging fields, we demonstrate its ability to iteratively segment human prostate glands from radiology imaging data.
