Does context matter in digital pathology?

TL;DR

This paper investigates the importance of contextual information in deep learning models for digital pathology, showing that context significantly influences model performance and stability, aligning with histopathologists' practices.

Contribution

The study demonstrates that context is crucial for accurate and stable predictions in digital pathology models, highlighting potential issues with partial context and model reliability.

Findings

Model performance drops with limited context

Models behave unstably with varying context sizes

Contextual information improves prediction accuracy

Abstract

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.