Pathology-Constrained Diffusion Models for Early Cancer Detection Using Medical Imaging

Early detection of cancer significantly improves survival rates; however, the scarcity of high-quality annotated medical imaging data limits the performance of deep learning models. Traditional data augmentation techniques fail to capture complex biological variations present in pathological images. In this study, we propose a Pathology-Constrained Diffusion Model (PCDM) framework for generating realistic and clin- ically meaningful synthetic medical images to enhance early cancer detection. The proposed approach integrates conditional diffusion models with pathology-aware constraints to preserve critical cellular structures such as nuclear morphology, chromatin patterns, and lesion boundaries. The generated images are used to augment training datasets for classification models, leading to improved performance. Extensive experiments conducted on cervical cancer datasets demonstrate significant improvements in accuracy, precision, recall, and F1-score compared to baseline methods. Furthermore, explainability analysis using Grad-CAM confirms that the model focuses on clinically relevant regions. The results highlight the potential of diffusion-based augmentation as a robust solution for low-data medical imaging scenarios.