Fully Automatic Content-Aware Tiling Pipeline for Pathology Whole Slide Images

TL;DR

This paper introduces an automatic deep learning pipeline combining CNNs and ViTs to detect artifacts in pathology whole slide images, improving quality assessment and ensuring more accurate computational pathology analysis.

Contribution

A novel supervised DL pipeline that fuses CNNs and ViTs for artifact detection in WSIs, outperforming existing methods in accuracy and generalization.

Findings

Achieved over 95% accuracy, precision, recall, and F1 score in artifact detection.

Outperformed state-of-the-art methods in quantitative evaluations.

Demonstrated strong generalization across tissue types and scanning conditions.

Abstract

In recent years, the use of deep learning (DL) methods, including convolutional neural networks (CNNs) and vision transformers (ViTs), has significantly advanced computational pathology, enhancing both diagnostic accuracy and efficiency. Hematoxylin and Eosin (H&E) Whole Slide Images (WSI) plays a crucial role by providing detailed tissue samples for the analysis and training of DL models. However, WSIs often contain regions with artifacts such as tissue folds, blurring, as well as non-tissue regions (background), which can negatively impact DL model performance. These artifacts are diagnostically irrelevant and can lead to inaccurate results. This paper proposes a fully automatic supervised DL pipeline for WSI Quality Assessment (WSI-QA) that uses a fused model combining CNNs and ViTs to detect and exclude WSI regions with artifacts, ensuring that only qualified WSI regions are used to build DL-based computational pathology applications. The proposed pipeline employs a pixel-based segmentation model to classify WSI regions as either qualified or non-qualified based on the presence of artifacts. The proposed model was trained on a large and diverse dataset and validated with internal and external data from various human organs, scanners, and H&E staining procedures. Quantitative and qualitative evaluations demonstrate the superiority of the proposed model, which outperforms state-of-the-art methods in WSI artifact detection. The proposed model consistently achieved over 95% accuracy, precision, recall, and F1 score across all artifact types. Furthermore, the WSI-QA pipeline shows strong generalization across different tissue types and scanning conditions.