Clever Hans effect found in a widely used brain tumour MRI dataset.

Machine learning is revolutionising medical image analysis, and clearly the future of the field lies in this direction. However, with increasing automation there is a danger of misunderstanding or misinterpreting models. In this paper, we expose an underlying bias in a commonly used publicly available brain tumour MRI dataset. We propose that this is due to implicit radiologist input in the selection of the 2D slices. Through several experiments we show how this bias allows us to achieve a high tumour classification accuracy, even with no information regarding the tumour itself. No other papers that use the dataset mention this bias. These findings demonstrate the importance of understanding machine learning models and their medical context, and the perils of not doing so.