An Explainable Patch-Based Deep Learning Framework for Marker-Specific Computational Assessment of Ovarian Autophagy in Digital Histopathology

Autophagy is essential for ovarian physiology and is implicated in pathological conditions such as Polyendocrine Metabolic Ovarian Syndrome (PMOS). The evaluation of autophagy-related markers in histopathological images is often subjective and observer-dependent. This study presents an explainable computational pathology framework that integrates patch-based deep learning with Grad-CAM-based visual interpretation to enable objective and reproducible analysis of autophagy-related immunohistochemical (IHC) staining in rat ovarian tissue. Beclin-1, LC3, and p62-stained rat ovarian tissue images from control, PMOS, bee bread (PERGA), and PERGA + PMOS groups were analyzed using a patch-based deep learning approach. Multiple convolutional neural network architectures, including DenseNet121, EfficientNetV2B0, and ConvNeXtTiny, were trained and evaluated, and their outputs were combined through an ensemble strategy. Image-level predictions were generated by averaging patch-level probabilities, and Grad-CAM was used to visualize image regions influencing model decisions. The LC3-based ensemble model achieved the highest overall image-level classification performance, with an F1-score of 78.28% and an AUC of 88.15%. In contrast, the Beclin-1 DenseNet121 model demonstrated the highest discrimination performance based on AUC, reaching 93.63%. Models trained on p62 images showed lower classification performance, likely due to the heterogeneous staining characteristics of p62 expression. Grad-CAM visualizations revealed that model activation was predominantly localized to DAB-positive and tissue-informative regions, supporting the interpretability of the proposed framework. In summary, this approach enables objective, reproducible, and interpretable analysis of autophagy-related histopathological images. The findings suggest that patch-based deep learning combined with explainable artificial intelligence offers a promising computational tool for assessing autophagy-related alterations in rat ovarian tissue.