Deep Blur Multi-Model -- a strategy to mitigate the impact of image blur on deep learning model performance in histopathology image analysis

AI-based analysis of histopathology whole slide images (WSIs) is central in computational pathology. However, image quality can impact model performance. Here, we investigate to what extent unsharp areas of WSIs impact deep convolutional neural network classification performance. We propose a multi-model approach, i.e. DeepBlurMM, to alleviate the impact of unsharp image areas and improve the model performance. DeepBlurMM uses the sigma cut-offs to determine the most suitable model for predicting tiles with various levels of blurring within a single WSI, where sigma is the standard deviation of the Gaussian distribution. Specifically, the cut-offs categorise the tiles into sharp or slight blur, moderate blur, and high blur. Each blur level has a corresponding model to be selected for tile-level predictions. Throughout the simulation study, we demonstrated the application of DeepBlurMM in a binary classification task for breast cancer Nottingham Histological Grade 1 vs 3. Performance, evaluated over 5-fold cross-validation, showed that DeepBlurMM outperformed the base model under moderate blur and mixed blur conditions. Unsharp image tiles (local blurriness) at prediction time reduced model performance. The proposed multi-model approach improved performance under some conditions, with the potential to improve quality in both research and clinical applications.