RT Journal Article
SR Electronic
T1 DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 097246
DO 10.1101/097246
A1 Andrew J. Schaumberg
A1 S. Joseph Sirintrapun
A1 Hikmat A. Al-Ahmadie
A1 Peter J. Schüffler
A1 Thomas J. Fuchs
YR 2016
UL http://biorxiv.org/content/early/2016/12/29/097246.abstract
AB Modern digital pathology departments have grown to produce whole-slide image data at petabyte scale, an unprecedented treasure chest for medical machine learning tasks. Unfortunately. most digital slides are not annotated at the image level, hindering large-scale application of supervised learning. Manual labeling is prohibitive, requiring pathologists with decades of training and outstanding clinical service responsibilities. This problem is further aggravated by the United States Food and Drug Administration’s ruling that primary diagnosis must come from a glass slide rather than a digital image. We present the first end-to-end framework to overcome this problem, gathering annotations 111 a nonintrusive manner during a pathologist’s routine clinical work: (i) microscope-specific 3D-printed commodity camera mounts are used to video record the glass-slide-based clinical diagnosis process; (ii) after routine scanning of the whole slide, the video frames are registered to the digital slide; (iii) motion and observation time are estimated to generate a spatial and temporal saliency map of the whole slide. Demonstrating the utility of these annotations, we train a convolutional neural network that detects diagnosis-relevant, salient regions, then report accuracy of 85.15% in bladder and 91.50% in prostate, with 75.00% accuracy when training on prostate but predicting in bladder, despite different pathologists examining the different tissues.