Evaluating diffusion model generated synthetic histopathology image data against authentic digital pathology images

T. Rai; C. Gola; M. Hernández; S. Fingerhood; J. Marrero; P. Diaz-Santana; G. Giglia; A. Morisi; B. Bacci; S. A. Thomas; L. Ressel; N. Bacon; N. Papachristou; A. Cook; R. La Ragione; K. Wells

doi:10.1117/12.3006285

3 April 2024 Evaluating diffusion model generated synthetic histopathology image data against authentic digital pathology images

T. Rai, C. Gola, M. Hernández, S. Fingerhood, J. Marrero, P. Diaz-Santana, G. Giglia, A. Morisi, B. Bacci, S. A. Thomas, L. Ressel, N. Bacon, N. Papachristou, A. Cook, R. La Ragione, K. Wells

Author Affiliations +

Proceedings Volume 12933, Medical Imaging 2024: Digital and Computational Pathology; 129331A (2024) https://doi.org/10.1117/12.3006285
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

This study explores the efficacy of diffusion probabilistic models for generating synthetic histopathological images, specifically canine Perivascular Wall Tumours (cPWT), to supplement limited datasets for deep learning applications in digital pathology. This research evaluates an open-source medical domain-focused diffusion model called Medfusion, where the model was trained on a small (1,000 patches) and a large dataset (17,000 patches) of cPWT images to compare performance on the different sized datasets. A Receiver Operating Characteristic (ROC) study was implemented to investigate the ability of six veterinary medical professionals and pathologists to discern between generated and real cPWT patch images. The participants engaged in two separate rounds, where each round corresponded to models that had been trained on the two different sized datasets. The ROC study revealed mean average Area Under the Curve (AUC) values close to 0.5 for both rounds. The results from this study suggests that diffusion models can create histopathological patch images that are convincingly realistic where our participants often struggled to reliably differentiate between generated and real images. This underscores the potential of these models as a valuable tool for augmenting digital pathology datasets.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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T. Rai, C. Gola, M. Hernández, S. Fingerhood, J. Marrero, P. Diaz-Santana, G. Giglia, A. Morisi, B. Bacci, S. A. Thomas, L. Ressel, N. Bacon, N. Papachristou, A. Cook, R. La Ragione, and K. Wells "Evaluating diffusion model generated synthetic histopathology image data against authentic digital pathology images", Proc. SPIE 12933, Medical Imaging 2024: Digital and Computational Pathology, 129331A (3 April 2024); https://doi.org/10.1117/12.3006285

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