User study of meaningfulness of generated DVCs.

<p>A. We asked <i>n</i> = 5 ophthalmologists to classify a given set of fundus images into healthy and referable DR categories. The image set contained both real fundus images (indicated by green outline) and generated DVCs (blue outline). B. Overall fraction of correctly graded im...

Full description

Saved in:

Bibliographic Details
Main Author:	Indu Ilanchezian (21369996) (author)
Other Authors:	Valentyn Boreiko (21369999) (author), Laura Kühlewein (18718849) (author), Ziwei Huang (192096) (author), Murat Seçkin Ayhan (21370002) (author), Matthias Hein (3861562) (author), Lisa Koch (3299613) (author), Philipp Berens (391695) (author)
Published:	2025
Subjects:	Medicine Cell Biology Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified retinal imaging based optical coherence tomography imaging based specialties answer counterfactual queries adversarially robust classifiers combining diffusion models retinal image structure resolution medical images create counterfactual images unconditional diffusion model subject &# 8217 method generated counterfactuals meaningful counterfactuals even generated counterfactuals diffusion model meaningful counterfactuals fundus image clinically meaningful ai model real images train ordinary related features patient education necessary disease illustrating answers expert study disease lesions disease categories diabetic retinopathy clinical settings classify healthy classifier guidance chance probability ai algorithm
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

User study of meaningfulness of generated DVCs.

Similar Items