A Lightweight, Interpretable Deep Learning System for Automated Detection of Cervical Adenocarcinoma In Situ (AIS)
This addresses the problem of early detection of AIS for healthcare providers, with potential applications in screening and low-resource settings, but it is incremental as it applies existing methods to a specific medical dataset.
The study tackled the challenge of accurately diagnosing cervical adenocarcinoma in situ (AIS) by developing a deep learning system that achieved an overall accuracy of 0.7323 and F1-scores of 0.75 for Abnormal and 0.71 for Normal classes.
Cervical adenocarcinoma in situ (AIS) is a critical premalignant lesion whose accurate histopathological diagnosis is challenging. Early detection is essential to prevent progression to invasive cervical adenocarcinoma. In this study, we developed a deep learning-based virtual pathology assistant capable of distinguishing AIS from normal cervical gland histology using the CAISHI dataset, which contains 2240 expert-labeled H&E images (1010 normal and 1230 AIS). All images underwent Macenko stain normalization and patch-based preprocessing to enhance morphological feature representation. An EfficientNet-B3 convolutional neural network was trained using class-balanced sampling and focal loss to address dataset imbalance and emphasize difficult examples. The final model achieved an overall accuracy of 0.7323, with an F1-score of 0.75 for the Abnormal class and 0.71 for the Normal class. Grad-CAM heatmaps demonstrated biologically interpretable activation patterns, highlighting nuclear atypia and glandular crowding consistent with AIS morphology. The trained model was deployed in a Gradio-based virtual diagnostic assistant. These findings demonstrate the feasibility of lightweight, interpretable AI systems for cervical gland pathology, with potential applications in screening workflows, education, and low-resource settings.