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The article mentioned they only had a dataset of 2,186 slides. Isn't this a _tiny_ dataset to do any learning on images? They also mentioned they blew up the pr
by tvladeck 10y ago
The article mentioned they only had a dataset of 2,186 slides. Isn't this a _tiny_ dataset to do any learning on images? They also mentioned they blew up the predictors to over 10,000 features. This sounds like a recipe for overfitting to me. I can't access the actual paper (getting a 500 error), but does anyone know how they built these algorithms?
- niels_olson 10y agoThe images are 1-5 gb each. They tile them. Then they did some simple sorting (remove all tiles with lots of white), (emphasize tiles with lots of blue), then they did random forests, survival models and k-fold cross validation. The test set is interesting: they used a completely unrelated dataset of tissue microarray spots. This are tiny, 1-2 mm circles. Whereas the TCGA images are full size tissue cassettes, up to 1" on the short side.