Explaining Negative Classifications of AI Models in Tumor Diagnosis

Using AI models in healthcare is gaining popularity. To improve clinician confidence in the results of automated triage and to provide further information about the suggested diagnosis, an explanation produced by a separate post-hoc explainability<br/>tool often accompanies the classification of an AI<br/>model. If no abnormalities are detected, however,<br/>it is not clear what an explanation should be. A<br/>human clinician might be able to describe certain<br/>salient features of tumors that are not in scan, but<br/>existing Explainable AI (XAI) tools cannot do that,<br/>as they cannot point to features that are absent<br/>from the input. In this paper, we present a definition of and algorithm for providing explanations<br/>of absence; that is, explanations of negative classifications in the context of healthcare AI.<br/>Our approach is rooted in the concept of explanations in actual causality. It uses the model as a<br/>black-box and is hence portable and works with<br/>proprietary models. Moreover, the computation<br/>is done in the preprocessing stage, based on the<br/>model and the dataset. During the execution, the algorithm only projects the precomputed explanation<br/>template on the current image.<br/>We implemented this approach in a tool, NITO, and<br/>trialed it on a number of medical datasets to demonstrate its utility on the classification of solid tumors.<br/>We discuss the differences between the theoretical<br/>approach and the implementation in the domain<br/>of classifying solid tumors and address the additional complications posed by this domain. Finally,<br/>we discuss the assumptions we make in our algorithm and its possible extensions to explanations<br/>of absence for general image classifiers.