R25 Section 5 - General Imaging

R25 VOICE - Section 5 - General Imaging Yin Aphinyanaphongs Papers discussed in this Section 5 Podcast: Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau & Sebastian Thrun. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (02 February 2017) doi:10.1038/nature21056 Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402–2410. doi:10.1001/jama.2016.17216 Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, Matthew P. Lungren, Andrew Y. Ng. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv:1711.05225 Podcast Contents Why These Papers? Dermatology Paper Concepts Inception v3 Pretraining t-SNE Comparison to humans Combining clinical data with imaging CheXnet Concepts Densenet Pretraining Horizontal flipping Class Activation Mappings Implications of downscaling Retinopathy Paper Human Comparison Different Cameras Concepts Pretraining Multitask -single network, multiple outputs Early stopping criteria Ensemble Learning Curves What is the Model Learning?