Shifting the Focus of Digital Pathology: The Raising Relevance of Pre-Processing Phase Over Model Complexity

Abstract

Recent trends in computational pathology favour increasingly complex deep learning architectures, raising the question of whether such complexity is necessary for routine diagnostic tasks. This study challenges this assumption through a comprehensive analysis of the relationship between model complexity, data pre-processing, and performance across four fundamental digital pathology tasks: nuclei counting, steatosis quantification, glomeruli detection, and Ki67 proliferation index (PI) assessment. We evaluated five deep learning models of varying complexity (lightweight: MobileNetV2, U-Net, and more complex: ConvNeXt, K-Net, and Swin Transformer) combined with different image pre-processing techniques. To evaluate model performance without extensive ground truth (GT) annotations, we introduced a validation strategy utilizing the relative absolute deviation (RAD) between network predictions and correlation of performance metrics. Our findings demonstrate that pre-processing strategies, particularly stain normalization (NORM), can be more impactful than model complexity, reducing error rates by up to 50% compared to processing original (ORIG) images. With appropriate pre-processing, lightweight models achieved comparable or superior results to complex models while reducing processing times by up to 40%. Only specific tasks involving complex morphological features, such as glomeruli detection, significantly benefited from more sophisticated architectures. This study provides an evidence-based framework for selecting optimal model-pre-processing combinations in clinical settings, suggesting that investing in pre-processing pipelines rather than model complexity may be more beneficial for routine computational pathology applications.