Ignite Creation Date:
2024-10-26 @ 3:37 PM
Last Modification Date:
2024-10-26 @ 3:37 PM
Study NCT ID:
NCT06542783
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-07-23
Brief Title:
Realistic in Generation of HEp-2 Cell Images Using Latent Diffusion Models a Multi-center Visual Turing Test
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Evaluating the Realism of ANA HEp-2 Cell Images Synthesized Using Latent Diffusion Models A Multi-center Visual Turing Test
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
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
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
Detailed Description:
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 httpsturingrednoblenet 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
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 httpsturingrednoblenet 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None