qurAI

Information

Location

Netherlands

Website

https://qurai.amsterdam/

Editors

ivanai

csangut

Activity Overview

EZ loss AMC

Reader Study

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EZ loss annotation

Reader Study

OCT

AIROGS

Challenge

Artificial Intelligence for RObust Glaucoma Screening Challenge

Fluid Segmentation in Retinal Optical Coherence Tomography (OCT)

Algorithm

Segments intraretinal fluid, subretinal fluid, and pigment epithelial detachments in Optical Coherence Tomography scans. Optimized for Spectralis, Cirrus and Topcon scanners.

AIROGS Baseline

Algorithm

A baseline algorithm for the AIROGS challenge

AIROGS Classifier

Algorithm

A end to end deep learning based system to find referable glaucoma and non-referable glaucoma.

Age-related macular degeneration (AMD) Staging in Optical Coherence Tomography (OCT) with UBIX for Increased Reliability

Algorithm

Deep learning models for optical coherence tomography (OCT) classification often perform well on data from scanners that were also used during training. However, when these models are applied to data from different vendors, their performance tends to drop substantially. Artifacts that only occur within scans from specific scanners are major causes of this poor generalizability. We aimed to improve this generalizability of deep learning models for OCT classifi- cation. To reduce the effect of vendor-specific artifacts, we propose Uncertainty-Based Instance eXclusion (UBIX), of which we define a hard and a soft variant. UBIX aims to suppress the contributions of B-scans with unseen artifacts to the final OCT-level outputs. Suppression is based on out-of-distribution detection of B-scans, which are instances in our multiple instance learning approach.