Efficient Special Stain Classification

TL;DR

This study compares two automated methods for classifying special stains in histopathology slides, demonstrating that a lightweight thumbnail-based approach offers high accuracy, better generalization, and significantly increased processing speed for clinical quality control.

Contribution

The paper introduces a scalable thumbnail-based stain classification method that outperforms traditional MIL in speed and generalization for routine pathology workflows.

Findings

Thumbnail approach achieved high accuracy (macro F1: 0.953)

Thumbnail method increased throughput by two orders of magnitude

External data showed better generalization with thumbnail model

Abstract

Stains are essential in histopathology to visualize specific tissue characteristics, with Haematoxylin and Eosin (H&E) serving as the clinical standard. However, pathologists frequently utilize a variety of special stains for the diagnosis of specific morphologies. Maintaining accurate metadata for these slides is critical for quality control in clinical archives and for the integrity of computational pathology datasets. In this work, we compare two approaches for automated classification of stains using whole slide images, covering the 14 most commonly used special stains in our institute alongside standard and frozen-section H&E. We evaluate a Multi-Instance Learning (MIL) pipeline and a proposed lightweight thumbnail-based approach. On internal test data, MIL achieved the highest performance (macro F1: 0.941 for 16 classes; 0.969 for 14 merged classes), while the thumbnail approach remained competitive (0.897 and 0.953, respectively). On external TCGA data, the thumbnail model generalized best (weighted F1: 0.843 vs. 0.807 for MIL). The thumbnail approach also increased throughput by two orders of magnitude (5.635 vs. 0.018 slides/s for MIL with all patches). We conclude that thumbnail-based classification provides a scalable and robust solution for routine visual quality control in digital pathology workflows.