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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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12 results for “macular edema”

Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema Gradingeess.IV

Deep learning system improves diabetic retinopathy and macular edema grading.

problem Manual screening of diabetic retinopathy and macular edema images is labor-intensive and error-prone.

method Used deep learning on fundus images, achieving comparable or better performance than previous studies.

result Deep learning system can accurately classify images according to clinical grading scales.

diabetic retinopathy macular edema deep learning image analysis screening

2019-04-16abs ↗pdf ↗

Evaluation of Transfer Learning for Classification of: (1) Diabetic Retinopathy by Digital Fundus Photography and (2) Diabetic Macular Edema, Choroidal Neovascularization and Drusen by Optical Coherence Tomographycs.LG

This study investigates transfer learning for medical image classification.

problem Limited data for training deep neural networks in medical domains.

method Transfer learning using various DNNs for diabetic retinopathy and macular edema.

result Transfer learning is feasible and promising for medical image classification.

transfer learning deep learning diabetic retinopathy diabetic macular edema ophthalmology

2019-01-26abs ↗pdf ↗

Application of Deep Learning in Fundus Image Processing for Ophthalmic Diagnosis -- A Reviewcs.CV

An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented. We also review various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, blood vessels and retinal layer as well as detection…

2018-12-09abs ↗pdf ↗

Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learningcs.CV

Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become increasingly important to detect center-involved diabetic macular edema (ci-DME). However, center-involved diabetic macular edema is diagnosed us…

2018-10-18abs ↗pdf ↗

Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCTeess.IV

Bayesian U-Net exploits epistemic uncertainty for anomaly detection in retinal OCT images.

problem Anomaly detection in retinal OCT images using weak labels of healthy anatomy.

method Bayesian U-Net trained on weak labels of healthy anatomy, using Monte Carlo dropout for uncertainty estimation, and post-processing to transfer uncertainty to anomaly segmentations.

result Achieved a Dice index of 0.789 in an independent test set of AMD cases.

retinal OCT anomaly detection bayesian deep learning epistemic uncertainty weak labels

2019-05-29abs ↗pdf ↗

retina-VAE: Variationally Decoding the Spectrum of Macular Diseaseeess.IV

Retina-VAE models macular disease spectrum using clinical data.

problem Representing the spectrum of macular diseases clinically.

method Variational autoencoder (VAE) model trained on patient profiles.

result Latent vectors cluster into 14 subtypes, suggesting treatment responses.

macular disease variational autoencoder latent vectors clinical data treatment responses

2019-07-11abs ↗pdf ↗

Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degenerationstat.ML

Objective: The advent of Electronic Medical Records (EMR) with large electronic imaging databases along with advances in deep neural networks with machine learning has provided a unique opportunity to achieve milestones in automated image analysis. Optical coherence tomography (OCT) is the most commonly obtained imagin…

2016-12-15abs ↗pdf ↗

Improvement of Multiparametric MR Image Segmentation by Augmenting the Data with Generative Adversarial Networks for Glioma Patientseess.IV

Study generates synthetic MR images to improve glioma segmentation.

problem Scarce annotated medical image data for glioma patients.

method Used generative adversarial networks (GANs) to create synthetic MR images.

result Synthetic images improved U-Net segmentation performance.

glioma brain tumor MR imaging GANs image segmentation

2019-10-01abs ↗pdf ↗

Development and Validation of a Novel Prognostic Model for Predicting AMD Progression Using Longitudinal Fundus Imagescs.LG

Deep learning predicts AMD progression from longitudinal fundus images.

problem Predicting future stages of age-related macular degeneration (AMD).

method InceptionV3 feature vectors, interval scaling, Recurrent Neural Network.

result 0.878 sensitivity, 0.887 specificity, 0.950 AUC.

deep learning prognostic model AMD fundus images macular degeneration

2020-07-10abs ↗pdf ↗

Localization of Critical Findings in Chest X-Ray without Local Annotations Using Multi-Instance Learningcs.LG

New DL algorithm detects critical chest X-ray findings without manual annotations.

problem Lack of explainability and manual annotation costs for DL models in medical imaging.

method Multi-instance learning approach to jointly classify and localize critical findings in CXR.

result Competitive classification results on three CXR datasets.

chest x-ray critical findings multi-instance learning deep learning explainability

2020-01-23abs ↗pdf ↗

tdCoxSNN: Time-Dependent Cox Survival Neural Network for Continuous-time Dynamic Predictionstat.ML

Proposes a neural network for dynamic risk prediction of AMD using longitudinal fundus images.

problem Dynamic risk prediction for progressive eye disorders like AMD.

method tdCoxSNN, a time-dependent Cox survival neural network integrating CNN.

result Demonstrates commendable predictive performance in AMD and PBC datasets.

neural network dynamic prediction time-dependent Cox longitudinal images progressive eye disorder

2023-07-12abs ↗pdf ↗

ICODEN: Ordinary Differential Equation Neural Networks for Interval-Censored Datacs.LG

ICODEN models survival data with interval-censored times using neural networks and ODEs.

problem Predicting time-to-event outcomes with interval-censored data, especially when models require strong assumptions or cannot handle high-dimensional predictors.

method ICODEN uses ordinary differential equations and deep neural networks to model the hazard function and cumulative hazard without proportional hazards assumption.

result ICODEN achieves satisfactory predictive accuracy across various simulation and real-world applications, handling high-dimensional predictors robustly.

interval-censored data survival analysis neural networks ordinary differential equations hazard modeling

2026-02-10abs ↗pdf ↗