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Boada, J.C. ; Umer, R.M. ; Marr, C.

CytoDiff: AI-Driven Cytomorphology Image Synthesis for Medical Diagnostics.

In: (Proceedings - 2025 IEEE/CVF International Conference on Computer Vision Workshops, ICCV-W 2025, 19-20 October 2025, Honolulu). 2025. 1136-1144 (Proceedings 2025 IEEE Cvf International Conference on Computer Vision Workshops Iccv W 2025)
DOI
Biomedical datasets are often constrained by stringent privacy requirements and frequently suffer from severe class imbalance. These two aspects hinder the development of accurate machine learning models. While generative AI offers a promising solution, producing synthetic images of sufficient quality for training robust classifiers remains challenging. This work addresses the classification of individual white blood cells, a critical task in diagnosing hematological malignancies such as acute myeloid leukemia (AML). We introduce CytoDiff, a stable diffusion model fine-tuned with LoRA weights and guided by few-shot samples that generates high-fidelity synthetic white blood cell images. Our approach demonstrates substantial improvements in classifier performance when training data is limited. Using a small, highly imbalanced real dataset, the addition of 5,000 synthetic images per class improved ResNet classifier accuracy from 27% to 78% (+51%). Similarly, CLIP-based classification accuracy increased from 62% to 77% (+ 15%). These results establish synthetic image generation as a valuable tool for biomedical machine learning, enhancing data coverage and facilitating secure data sharing while preserving patient privacy. Paper code is publicly available at https://github.com/JanCarreras24/CytoDiff.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Cytomorphology ; Diffusion Model ; Images Synthesis ; Medical Diagnosis
ISSN (print) / ISBN [9798331589882]
Konferenztitel Proceedings - 2025 IEEE/CVF International Conference on Computer Vision Workshops, ICCV-W 2025
Konferzenzdatum 19-20 October 2025
Konferenzort Honolulu
Quellenangaben Band: , Heft: , Seiten: 1136-1144 Artikelnummer: , Supplement: ,
Institut(e) Institute of AI for Health (AIH)