Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope

Andrew J. Schaumberg, S. Joseph Sirintrapun, Hikmat A. Al-Ahmadie, Peter J. Schüffler, Thomas J. Fuchs
doi: https://doi.org/10.1101/097246
Andrew J. Schaumberg
1Memorial Sloan Kettering Cancer Center and the Tri-Institutional Training Program in Computational Biology and Medicine New York, NY, USA
2Weill Cornell Graduate School of Medical Sciences, New York, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
S. Joseph Sirintrapun
3Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hikmat A. Al-Ahmadie
3Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Peter J. Schüffler
4Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Thomas J. Fuchs
3Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
4Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted December 29, 2016.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope
Andrew J. Schaumberg, S. Joseph Sirintrapun, Hikmat A. Al-Ahmadie, Peter J. Schüffler, Thomas J. Fuchs
bioRxiv 097246; doi: https://doi.org/10.1101/097246
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope
Andrew J. Schaumberg, S. Joseph Sirintrapun, Hikmat A. Al-Ahmadie, Peter J. Schüffler, Thomas J. Fuchs
bioRxiv 097246; doi: https://doi.org/10.1101/097246

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Pathology
Subject Areas
All Articles
  • Animal Behavior and Cognition (3698)
  • Biochemistry (7809)
  • Bioengineering (5689)
  • Bioinformatics (21330)
  • Biophysics (10595)
  • Cancer Biology (8199)
  • Cell Biology (11961)
  • Clinical Trials (138)
  • Developmental Biology (6777)
  • Ecology (10419)
  • Epidemiology (2065)
  • Evolutionary Biology (13900)
  • Genetics (9726)
  • Genomics (13094)
  • Immunology (8164)
  • Microbiology (20058)
  • Molecular Biology (7871)
  • Neuroscience (43147)
  • Paleontology (321)
  • Pathology (1280)
  • Pharmacology and Toxicology (2264)
  • Physiology (3362)
  • Plant Biology (7246)
  • Scientific Communication and Education (1315)
  • Synthetic Biology (2010)
  • Systems Biology (5547)
  • Zoology (1132)