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It reminds me of a similar story about using deep learning on medical imagery for diagnosis. In one study, it supposedly learned the to tell which device was used to take the images. Apparently that was somewhat correlated with the results as more serious / heavily suspected cases were sent to a specific hospital in the region.

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Similarly, I recall a talk by Daphne Koller where she talks about a heart attack predictor using X-rays as input. They found that stents were the biggest factor for positive prediction. Which is useless because the doctor would know if the patient had a stent.

A similar story I heard from colleagues in the field was about a CNN trained on images of tumors that ended up learning to identify images containing rulers that were placed there by a human for reference.

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