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Found 7 papers, 1 papers with code
Blood vessel segmentation in en-face OCTA images: a frequency based method
The results underline the visual evaluation.
An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans
Supervised deep learning models trained with standard loss functions are usually able to characterize only the most common disease appeareance from a training set, resulting in suboptimal performance and poor generalization when dealing with unseen lesions.
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.
Ranked #4 on
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on AIM-500
On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems
In two supervised learning problems, clinical image segmentation and music information classification, the application of our proposed augmented target loss functions increase the accuracy.
Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data
A multi-scale deep denoising autoencoder is trained on healthy images, and a one-class support vector machine identifies anomalies in new data.
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.
