An integrated iterative annotation technique for easing neural network training
in medical image analysis.
Authors Lutnick B, Ginley B, Govind D, McGarry SD, LaViolette PS, Yacoub R, Jain S,
Tomaszewski JE, Jen KY, Sarder P
Submitted By Pinaki Sarder on 6/18/2019
Status Published
Journal Nature machine intelligence
Year 2019
Date Published
Volume : Pages 1 : 112 - 119
PubMed Reference 31187088
Abstract 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.

Investigators with authorship
Pinaki SarderSUNY at Buffalo