Scientific Discovery by Generating Counterfactuals using Image Translation

Visual recognition models are increasingly applied toscientific domains such as, drug studies and medical diag-noses, and model explanation techniques play a critical rolein understanding the source of a model’s performance andmaking its decisions transparent. In this work we investi-gate if explanation techniques can also be used as a mech-anism for scientific discovery. We make two contributions,first we propose a framework to convert predictions from ex-planation techniques to a mechanism of discovery. Secondwe show how generative models in combination with black-box predictors can be used to generate hypotheses (withouthuman priors) that can be critically examined. With thesetechniques we study classification models on retinal fundusimages predicting Diabetic Macular Edema (DME). Essen-tially deep convolutional models on 2D retinal fundus im-ages can do nearly as well as ophthalmologists looking at3D scans, making this an interesting case study of clinicalrelevance. Our work highlights that while existing expla-nation tools are useful, they do not necessarily provide acomplete answer. With the proposed framework we are ableto bridge the gap between model’s performance and humanunderstanding of the underlying mechanism which is of vi-tal scientific interest.

• Machine Intelligence

• Machine Perception