Advanced Image Generation for Cancer and Stem Cell Biology Using Diffusion Models.

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Abstract

Deep learning has transformed medical image analysis, but progress in cancer and stem cell applications is often constrained by limited access to large, diverse, well-annotated imaging datasets. This bottleneck is especially acute for studies of tumor heterogeneity and cancer stem cell (CSC) biology, where rare phenotypes and dynamic cell-state transitions-frequently linked to stemness-associated transcriptional programs (e.g., OCT4, SOX2, NANOG)-benefit from high-quality imaging across many samples and conditions. At the same time, regulatory and practical barriers (patient privacy, acquisition cost, and uneven institutional data sharing) restrict dataset scale and reuse. Diffusion models offer a practical route to synthetic data expansion by generating high-fidelity synthetic images that retain salient radiologic and pathologic features. In this chapter, we present an end-to-end protocol for adapting latent diffusion (Stable Diffusion) to oncology imaging using DreamBooth fine-tuning with small numbers of representative images, coupled with text-to-image and image-to-image workflows to generate controlled variations across modalities and disease presentations (e.g., brain tumor MRI, breast cancer mammography/CESM). We also describe quantitative and qualitative evaluation strategies, including Fréchet Inception Distance (FID) benchmarking and expert review considerations, to assess realism and diversity. These methods enable cancer and stem cell biologists to augment training data for segmentation and classification, build shareable educational resources, and prototype analyses for rare tumors or stemness-enriched subtypes while potentially reducing reliance on direct sharing of patient images.

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