2022 Volume 40 Issue 5 Pages 218-225
Over the last decade, in pathology numerous reports agree that performance of artificial intelligence (AI) with convolutional neural networks in diagnostic imaging has exceeded that of humans. The consequent increase of expectations on AI has raised a problem of accountability: while human doctors are always responsible for their diagnosis, the same does not apply to an AI doctor. In particular, the decision process of the AI is concealed within a black box that does not provide the reason for which a certain diagnosis is reached. To address this problem, known as explainability, we propose the following method. The input image for the diagnosis contains many local features. Among them, features which are often seen in the lesion area and rarely in the normal area have a high content of information and should be highlighted as information-rich and likely evidence of the lesion. When we define “Information Density” as the total amount of information that features in each sector of the image carry, the sectors with large “Information Density” are identified as those belonging to the lesion. The results of this method on CAMELYON 16 images are consistent with the pathologistʼs diagnosis.
