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Found 5 papers, 3 papers with code
Evaluating sleep-stage classification: how age and early-late sleep affects classification performance
Sleep stage classification is a common method used by experts to monitor the quantity and quality of sleep in humans, but it is a time-consuming and labour-intensive task with high inter- and intra-observer variability.
Learning normal asymmetry representations for homologous brain structures
This paper introduces a novel method to learn normal asymmetry patterns in homologous brain structures based on anomaly detection and representation learning.
A ResNet is All You Need? Modeling A Strong Baseline for Detecting Referable Diabetic Retinopathy in Fundus Images
In this paper we propose to model a strong baseline for this task based on a simple and standard ResNet-18 architecture.
A deep learning model for brain vessel segmentation in 3DRA with arteriovenous malformations
While deep learning models have been applied for segmenting the brain vasculature in these images, they have never been used in cases with bAVMs.
Assessing Coarse-to-Fine Deep Learning Models for Optic Disc and Cup Segmentation in Fundus Images
In this paper we present a comprehensive analysis of different coarse-to-fine designs for OD/OC segmentation using 5 public databases, both from a standard segmentation perspective and for estimating the vCDR for glaucoma assessment.
