acecross-domain histological image classification of lung, cerebellum and adipose tissues using deep neural network encoders tested on external mixed human-and-animal cohort
Source article: Cross-Species Generalization and Comparative Performance Analysis of Deep Neural Network Architectures in Histological Image Classification
Abstract: 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…
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Ночь by V-shechka. CC BY 4.0 · https://creativecommons.org/licenses/by/4.0
On 2026-08-16, a peer-reviewed study reported a comparative evaluation of nine deep learning encoders for H&E histology classification. Models were trained on 4307 male rat tissue images and tested on a separate 600-image mixed human-and-animal cohort using frozen features and a linear probe, measuring accuracy, F1, kappa, ROC-AUC and inference time.
The results matter for diagnostic decision support because near-perfect internal performance did not transfer equally externally, highlighting cross-species generalization as a bottleneck. UNI2-h provided the most precise cross-domain results while ConvNeXt-Small offered the best speed-accuracy trade-off, but uncertainty remains about performance with full fine-tuning, broader organ types, and diverse human clinical populations.
- Study trained nine encoders on 4307 H&E stained images of male rat lung, cerebellum, and adipose tissues under Ege University ethical approval HADYEK 2026-06.
- External testing used 600 mixed human-and-animal images with frozen feature extraction plus linear probe evaluation.
- UNI2-h pathology foundation model achieved 97.4% F1 and 95.0% recall for lung tissue, the most diverse and difficult class.
- MobileNetV3-Small had lowest inference time at 9.4 ms on CPU and 12.5 ms on GPU, while ConvNeXt-Small was identified as optimal balance of speed and accuracy.
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.
The rundown
Researchers trained MobileNetV3-S, MobileNetV3-L, DenseNet121, DenseNet201, ConvNeXt-S, ConvNeXt-B, DINOv3 ViT-S, DINOv3 ViT-H+, and UNI2-h on 4307 H&E images of male rat lung, cerebellum and adipose, then tested on 600 mixed human-and-animal images using accuracy, sensitivity, specificity, F1, Cohen's kappa, ROC-AUC and per-image inference time.
Internal cross-validation was near-perfect for all models, but external results separated architectures; adipose and cerebellum remained at 95% F1 or higher for competitive models, while lung tissue exposed the largest gap, with UNI2-h on top and MobileNetV3-Small fastest at 9.4 ms CPU and 12.5 ms GPU.
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.
Sources
- Peer-reviewedMicroscopy Research and Technique2026-08-16
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