JustViT


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About

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Last updated:
April 20, 2024, 5:26 a.m.

Interfaces

This algorithm implements all of the following input-output combinations:

Inputs Outputs
1
    Stacked Color Fundus Images
    Multiple Referable Glaucoma Likelihoods
    Multiple Referable Glaucoma Binary Decisions
    Stacked Referable Glaucomatous Features

Challenge Performance

Date Challenge Phase Rank
April 20, 2024 JustRAIGS Development Phase 3
April 20, 2024 JustRAIGS Test Phase 7

Model Facts

Summary

Detailed description of the algorithm can be found here: https://github.com/TomaszKubrak/Glaucoma_classification_JustRAIGS

Mechanism

Details about the target population can be found in the description of the JustRAIGS dataset: https://www.sciencedirect.com/science/article/pii/S2666914523000325

The backbone of the architecture comprises four independent Vision Transformers (ViT), preceded by optic disc detection using YoloV8 and extensive image and dataset preprocessing. The architecture accepts fundus images as input and outputs stacked probabilities of glaucoma along with binary values and 10 diagnostic features.

Validation and Performance

Uses and Directions

This algorithm was developed for research purposes only.

Warnings

Common Error Messages

Information on this algorithm has been provided by the Algorithm Editors, following the Model Facts labels guidelines from Sendak, M.P., Gao, M., Brajer, N. et al. Presenting machine learning model information to clinical end users with model facts labels. npj Digit. Med. 3, 41 (2020). 10.1038/s41746-020-0253-3