Exploring Deep Learning Models for Small Histopathology Datasets: Segmentation and Classification of Glomerular Crescent Lesions with Ablation, Interpretability, and Calibration Analyses

Glomerular crescent lesions are critical indicators of severe kidney injury and are closely associated with disease progression. However, their automated identification remains challenging due to limited annotated data, class imbalance, and subtle morphological variations. This study proposes a comprehensive deep learning (DL) framework for segmentation and classification of glomerular crescent lesions in histopathology images, with emphasis on robustness under limited data conditions. The ISICDM2024 Challenge dataset is used for evaluation. For segmentation, several baseline models are first evaluated, including DeepLabV3, U-Net, Transformer-based U-Net, and a feature pyramid network (FPN) with a ResNet-34 backbone. Similarly, for classification, multiple baseline models are evaluated, including EfficientNetV2-B0, ResNet-50, DenseNet-121, hybrid CNNs, CTransPath, and RetCCL. Motivated by the strong performance of FPN with ResNet-34 and DenseNet-121, two customized models are developed, namely CrescentSegNet for segmentation and CrescentDenseNet for classification. Comprehensive ablation studies are conducted, and interpretability and reliability are assessed using Grad-CAM, saliency mapping, uncertainty estimation, calibration analysis, and t-SNE. Cross-dataset evaluation on SICAPv2 and BreaKHis 400 × confirms strong generalization and robustness. The proposed framework achieves competitive performance while maintaining efficiency and interpretability.