Open-source framework for detecting bias and overfitting for large pathology images.

ABSTRACT: Even foundational models trained on large-scale datasets may learn to rely on non-relevant artifacts such as background color or color intensity, leading to overfitting and/or bias. To ensure the robustness of deep learning applications, there is a need for methods to detect and remove the use of these artifacts. Existing debugging methods are often domain- and model-architecture-specific, and may be computationally expensive, hindering widespread use. We propose a model-architecture-agnostic framework to debug deep learning models. To demonstrate the utility of our framework, we test it using a widely used dataset from histopathology, which has been tested in other literature. The dataset features very large images that typically demand large computational resources. We demonstrate that t

SUBMITTER: Sildnes A 

PROVIDER: S-EPMC12919778 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature