Controllable Diffusion-Based Lesion Inpainting for Scalable Histopathology Data Augmentation
arXiv:2601.08127v2 Announce Type: replace-cross Abstract: Expert-annotated training data remains the critical bottleneck for AI in histopathology, particularly for rare pathologies where even dozens of cases may be unavailable. While data augmentation offers a solution, existing methods fail to generate sufficiently realistic lesion morphologies that preserve tissue-specific architectures. Here we present PathoGen, a diffusion-based generative model enabling controllable, high-fidelity lesion inpainting into benign histopathology images. We validate PathoGen across four datasets representing k
The development of sophisticated diffusion models has matured, enabling high-fidelity generative capabilities now being applied to specialized domains like medical imaging augmentation to address data scarcity.
This breakthrough addresses a critical bottleneck in deploying AI in histopathology, particularly for rare diseases, by creating realistic synthetic data, accelerating drug discovery and diagnostic tool development.
The ability to controllably generate realistic lesion morphologies means AI models can be trained more effectively even with limited real-world data, expanding the scope and reliability of AI in medical diagnostics.
- · AI in healthcare startups
- · Pharmaceutical companies
- · Pathologists and research institutions
- · Patients with rare diseases
- · Traditional manual pathology labs (if they don't integrate AI)
- · Companies relying on large, expensive real-world datasets as a moats
Improved diagnostic accuracy and accelerated research for rare diseases through synthetic data.
Reduced cost and time for AI model development in medical imaging, leading to broader AI adoption in healthcare.
Ethical and regulatory frameworks will need to evolve rapidly to manage the use and validation of AI models trained on synthetic medical data.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI