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
- Concepts
- CheXnet
- Concepts
- Densenet
- Pretraining
- Horizontal flipping
- Class Activation Mappings
- Implications of downscaling
- Concepts
- Retinopathy Paper
- Human Comparison
- Different Cameras
- Concepts
- Pretraining
- Multitask -single network, multiple outputs
- Early stopping criteria
- Ensemble
- Learning Curves
- What is the Model Learning?