Improving Diagnostic Accuracy for Oral Cancer with inpainting Synthesis Lesions Generated Using Diffusion Models
On the map: Diffusion models → Medical imaging and segmentation
Abstract (arXiv)
In oral cancer diagnostics, the limited availability of annotated datasets frequently constrains the performance of diagnostic models, particularly due to the variability and insufficiency of training data. To address these challenges, this study proposed a novel approach to enhance diagnostic accuracy by synthesizing realistic oral cancer lesions using an inpainting technique with a fine-tuned diffusion model. We compiled a comprehensive dataset from multiple sources, featuring a variety of oral cancer images. Our method generated synthetic lesions that exhibit a high degree of visual fidelity to actual lesions, thereby significantly enhancing the performance of diagnostic algorithms. The results show that our classification model achieved a diagnostic accuracy of 0.97 in differentiating between cancerous and non-cancerous tissues, while our detection model accurately identified lesion locations with 0.85 accuracy. This method validates the potential for synthetic image generation in medical diagnostics and paves the way for further research into extending these methods to other types of cancer diagnostics.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 9 of its 24 references that are in the map. First posted on arXiv 8 Aug 2025.
Builds on
9 of 24 references.
References · 9
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2023Segment Anythingcited by 8,158
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
- 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
- 2023Segment Anything in High Qualitycited by 373
- 2020Generative Adversarial Networks (GANs Survey): Challenges, Solutions, and Future Directionscited by 65
- 2024DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised h-transformcited by 44
- 2024Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challengescited by 24
- 2018Adversarial Inpainting of Medical Image Modalitiescited by 16
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026Generative AI-Based Data Augmentation for Oral Lesion Classification: The PhotoMOCI Dataset and Benchmark
- 2025Diffusion models applied to skin and oral cancer classification
- 2026PCaPaint: Prostate Cancer Inpainting by Mitigating Shortcut Learning
- 2023MAM-E: Mammographic synthetic image generation with diffusion models
- 2026Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions
- 2025Class-N-Diff: Classification-Induced Diffusion Model Can Make Fair Skin Cancer Diagnosis
- 2025Paired Image Generation with Diffusion-Guided Diffusion Models
- 2026Controllable Diffusion-Based Lesion Inpainting for Scalable Histopathology Data Augmentation