Evaluating the Realism of ANA HEp-2 Cell Images Synthesized Using Latent Diffusion Models: A Multi-center Visual Turing Test
Evaluating the Realism of ANA HEp-2 Cell Images Synthesized Using Latent Diffusion Models: A Multi-center Visual Turing Test
The objective of this prospective observational study is to rigorously examine the feasibility and efficacy of utilizing latent diffusion models for data augmentation in anti-nuclear antibody (ANA) Hep-2 cell immunofluorescence images. The main question it aims to answer is:
Can the application of such models potentially enhance the data quality, increase sample diversity, or improve the accuracy and efficiency of subsequent analytical processes (like disease diagnosis and classification) when utilized with ANA-related images?
A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real observations with generated in silico samples could lead to more robust analysis results and a higher reproducibility rate. Here, The investigators propose to use unsupervised learning with latent diffusion models for the realistic generation of ANA-IIF image data.
The investigators hypothesize that the the generation of ANA-IIF image will be realistic if it is hard to differentiate them (fake) from real (true) . To test this hypothesis, the investigators present a Multi-center Visual Turing tests (https://turing.rednoble.net/) in order to evaluate the quality of the generated (fake) images.
This experimental setup allows the investigators to validate the overall quality of the generated ANA-IIF images, which can then be used to (1) train cytopathologists for educational purposes, and (2) generate realistic samples to train deep networks with big data.
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