The development of Artificial Intelligence for healthcare is of great
importance. Models can sometimes achieve even superior performance to human
experts, however, they can reason based on spurious features. This is not
acceptable to the experts as it is expected that the models catch the valid
patterns in the data following domain expertise. In the work, we analyse
whether Deep Learning (DL) models for vision follow the histopathologists'
practice so that when diagnosing a part of a lesion, they take into account
also the surrounding tissues which serve as context. It turns out that the
performance of DL models significantly decreases when the amount of contextual
information is limited, therefore contextual information is valuable at
prediction time. Moreover, we show that the models sometimes behave in an
unstable way as for some images, they change the predictions many times
depending on the size of the context. It may suggest that partial contextual
information can be misleading.

开发医疗人工智能的重要性；通过分析深度学习模型在视觉领域中是否遵循组织学家实践，并证明在预测时上下文信息对模型的性能具有重要影响，部分上下文信息可能导致模型行为不稳定。