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Machine Learning Annotations
September 10, 2018
George Shih, MDCo-chair SIIM ML Committee
Associate Professor and Vice-Chair for Informatics Weill Cornell Radiology
https://hackernoon.com/%EF%B8%8F-big-challenge-in-deep-learning-training-data-31a88b97b282
15,000 exams from CXR14 entire dataset.
- all 5,300 pneumothorax
- random 5,000 normal
- random 5,000 not pneumothorax, not normal
crowdsourcing annotations
Crowdsourcing Pneumothorax Annotations using Machine Learning Annotations (MLA) on the NIH Chest X-ray DatasetSrinidhi G. Myadaboyina, MS, New York University, MD.ai
Yashovardhan Chaturvedi; Anouk Stein, MD; Leon Chen, MD; Stephen Borstelmann, MD; Ross Filice, MD; Paras C. Lakhani, MD; Maansi Parekh, MD; Prasanth Prasanna, MD; Alexandre Cadrin-Chênevert, MD; George Shih, MD, MS
Sources of Annotations
Human (radiologist) annotations
ML algorithms for generating annotations
Any ML algorithm output = annotations?
Standardizing AI Annotations - The DICOM Way
David A. Clunie, MBBS, FSIIM, PixelMed Publishing
Enabling Automated Search for Relevant Machine Learning Datasets
Steve G. Langer, PhD, CIIP, Mayo Clinic Rochester
Image Labeling for Deep Learning: Machine vs. Human
Curtis P. Langlotz, MD, PhD, FSIIM, Stanford University
Scaling up Image Annotation for Deep Learning: Standards, Labels from
Text, and Leveraging Multi-institutional Data
Daniel L. Rubin, MD, MS, FSIIM, Stanford University
Panel Discussion
Luminary Panel