aceCross-Species Generalization and Comparative Performance Analysis of Deep Neural Network Architectures in Histological Image Classification
Histological image classification plays a critical role in biomedical research and diagnostic processes. Advances in the field of deep learning present significant opportunities for enhancing diagnostic accuracy and developing automated decision support systems. This study aims to comparatively evaluate the out-of-distribution generalization and cross-domain classification performance of different deep neural network encoders. In this study, models were trained on an internal dataset of 4307 hematoxylin and eosi…
The pathology foundation model UNI2-h achieved the highest cross-domain accuracy on an external mixed human-and-animal cohort, including 97.4% F1 for difficult lung tissue classification.
Models that were near-perfect during internal cross-validation diverged significantly on the separate external cohort, showing reduced out-of-distribution generalization for cross-species histological classification.
Training was limited to male rat tissues from three organs, and evaluation used frozen feature extraction with linear probe only, which may not reflect full fine-tuning performance or broader human clinical diversity.
Evidence
- Peer-reviewedMicroscopy Research and Technique2026-08-16
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Truvace Impact Record TRV-2026-0796, v1: “Cross-Species Generalization and Comparative Performance Analysis of Deep Neural Network Architectures in Histological Image Classification.” Truvace, 2026-08-17. /record/TRV-2026-0796 (accessed at citation time). sha256 96d366aaf59d8968…
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