Picture for Bianca S. Gerendas

Bianca S. Gerendas

Blood vessel segmentation in en-face OCTA images: a frequency based method

Add code
Sep 13, 2021
Figure 1 for Blood vessel segmentation in en-face OCTA images: a frequency based method
Figure 2 for Blood vessel segmentation in en-face OCTA images: a frequency based method
Figure 3 for Blood vessel segmentation in en-face OCTA images: a frequency based method
Figure 4 for Blood vessel segmentation in en-face OCTA images: a frequency based method
Viaarxiv icon

An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans

Add code
Aug 02, 2019
Figure 1 for An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans
Figure 2 for An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans
Figure 3 for An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans
Figure 4 for An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans
Viaarxiv icon

Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation

Add code
Jan 25, 2019
Figure 1 for Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
Figure 2 for Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
Figure 3 for Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
Viaarxiv icon

U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans

Add code
Jan 23, 2019
Figure 1 for U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans
Figure 2 for U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans
Figure 3 for U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans
Figure 4 for U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans
Viaarxiv icon

On orthogonal projections for dimension reduction and applications in variational loss functions for learning problems

Add code
Jan 22, 2019
Figure 1 for On orthogonal projections for dimension reduction and applications in variational loss functions for learning problems
Figure 2 for On orthogonal projections for dimension reduction and applications in variational loss functions for learning problems
Figure 3 for On orthogonal projections for dimension reduction and applications in variational loss functions for learning problems
Figure 4 for On orthogonal projections for dimension reduction and applications in variational loss functions for learning problems
Viaarxiv icon

Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data

Add code
Oct 31, 2018
Figure 1 for Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data
Figure 2 for Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data
Figure 3 for Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data
Figure 4 for Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data
Viaarxiv icon

Identifying and Categorizing Anomalies in Retinal Imaging Data

Add code
Dec 02, 2016
Figure 1 for Identifying and Categorizing Anomalies in Retinal Imaging Data
Figure 2 for Identifying and Categorizing Anomalies in Retinal Imaging Data
Viaarxiv icon