DeepScope: Nonintrusive whole slide saliency annotation and prediction from pathologists at the microscope Conference Paper
| Authors: | Schaumberg, A. J.; Sirintrapun, S. J.; Al-Ahmadie, H. A.; Schüffler, P. J.; Fuchs, T. J. |
| Title: | DeepScope: Nonintrusive whole slide saliency annotation and prediction from pathologists at the microscope |
| Conference Title: | 13th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2016) |
| 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 in 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 91.40% in prostate, with 75.00% accuracy when training on prostate but predicting in bladder, despite different pathologists examining the different tissues. When training on one patient but testing on another, AUROC in bladder is 0.79 ± 0.11 and in prostate is 0.96 ± 0.04. Our tool is available at https://bitbucket.org/aschaumberg/deepscope. © Springer International Publishing AG 2017. |
| Keywords: | medical imaging; artificial intelligence; diagnosis; urology; glass; bioinformatics; computer graphics; clinical diagnosis; learning systems; neural networks; convolutional neural network; 3d printers; clinical services; commodity cameras; digital pathologies; large-scale applications; united states food and drug administrations; whole slide images |
| Journal Title | Lecture Notes in Computer Science |
| Volume: | 10477 |
| Conference Dates: | 2016 Sep 1-3 |
| Conference Location: | Stirling, UK |
| ISBN: | 0302-9743 |
| Publisher: | Springer |
| Date Published: | 2017-01-01 |
| Start Page: | 42 |
| End Page: | 58 |
| Language: | English |
| DOI: | 10.1007/978-3-319-67834-4_4 |
| PROVIDER: | scopus |
| PMCID: | PMC5870882 |
| PUBMED: | 29601065 |
| DOI/URL: | |
| Notes: | Chapter in "Computational Intelligence Methods for Bioinformatics and Biostatistics" (ISBN: 978-3-319-67833-7) -- Conference Paper -- Export Date: 4 December 2017 -- Source: Scopus |
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655Al-Ahmadie -
202Sirintrapun -
29Fuchs -
17Schueffler
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