Computer Vision Models for Automated Histopathological Analysis

Abstract

Automated histopathological analysis -- the systematic application of computer vision algorithms to whole-slide histological images for classification, segmentation, and quantification of pathological features -- has the potential to transform regenerative medicine research and clinical practice by providing reproducible, quantitative, and scalable tissue assessment at throughput impossible with manual pathology. While deep learning has achieved expert-level performance in select oncology histopathology tasks, the breadth of computer vision methods applicable to regenerative medicine histopathology -- spanning multi-scale feature extraction, cross-stain feature transfer, multi-task learning, and active learning for annotation efficiency -- has not been systematically evaluated in the regenerative medicine context. This paper proposes the Automated Histopathology Computer Vision (AHCV) framework, a comprehensive evaluation of computer vision architectures and training strategies for regenerative medicine histopathological analysis, comprising five methodological components: a multi- scale vision transformer (MSVT) for whole-slide image classification; a cross-stain feature transfer network (CSFTN) that transfers knowledge between histological stain types; a multi-task histopathology network (MTHN) that jointly optimises classification, segmentation, and grading tasks; an active learning annotation strategy (ALAS) that minimises expert annotation burden while maximising model performance; and a morphological explainability module (MEM) that generates pathologist-interpretable explanations for model predictions. AHCV is evaluated on the RegenHisto dataset (Costa, 2025) extended with 1,284 additional annotations from six regenerative tissue types. MSVT achieves whole-slide classification AUC = 0.942 (SD = 0.018) -- outperforming ResNet-50 (0.884) and vision transformer ViT-L (0.922). CSFTN enables H&E-trained; models to achieve 88.4% of multi-stain performance using only H&E; training data. ALAS reduces annotation requirements by 64.2% while maintaining 96.4% of full-annotation performance. The study contributes the AHCV specification, a systematic benchmark of computer vision architectures for regenerative histopathology, and the RegenHisto-Extended dataset.