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Found 5 papers, 0 papers with code
U-Net with spatial pyramid pooling for drusen segmentation in optical coherence tomography
In the second approach, the surrounding retinal layers (outer boundary retinal pigment epithelium (OBRPE) and Bruch's membrane (BM)) are segmented and the remaining space between these two layers is extracted as drusen.
Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography
We also introduce connections between each class-specific branch and the additional decoder to increase the regularization effect of this surrogate task.
Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
Among the several sources of variability the ML models have to deal with, a major factor is the acquisition device, which can limit the ML model's generalizability.
U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans
In this paper, we introduce a Bayesian deep learning based model for segmenting the photoreceptor layer in pathological OCT scans.
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Identifying and Categorizing Anomalies in Retinal Imaging Data
The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice.
