arXiv:2610.30775v1 Announce Type: new Abstract: Histopathology images are recognized as a reliable and comprehensive modality for Deep Learning (DL) based diagnosis. However, effectiveness is constrained by limited large-scale annotated datasets, as collecting expert-labeled medical data is costly and time-intensive. Recent advances in generative models, particularly diffusion models, demonstrate strong capabilities in synthesizing medically relevant content for dataset augmentation. Existing generative frameworks treat histopathology patches as generic natural images, failing to enforce biological constraints such as histomorphological structures and heterogeneous staining patterns essential for valid synthesis. We propose a guided wavelet diffusion model (GWDM) framework that captures image details at multiple scales using wavelets and leverages controllable conditioning based on domain-specific histopathological features (e.g., tissue patterns and color variations). We further formulate a histomorphological guidance loss applied to real and generated patches. A loss ramping strategy gradually introduces these constraints during training, ensuring stable optimization and robust modeling of tissue architecture. GWDM was evaluated on three benchmark datasets (BCSS, MoNuSeg, and PAIP2019), consistently outperforming state-of-the-art generative methods. Ablation studies confirmed that domain-guided conditioning, perceptual loss, and structural similarity loss each contribute meaningfully to synthesis quality. Downstream nuclei and tissue segmentation experiments further validated the clinical utility of GWDM-generated images, demonstrating consistent performance improvements when synthetic data augments real training sets. This work shows that domain-specific conditioning and dual-loss optimization generate realistic histopathology patches, reducing data scarcity and improving clinical downstream segmentation performance.