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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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0111 · Dec 201619922001200920172026
6 results for “ophthalmology”
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 learningdeep learningdiabetic retinopathydiabetic macular edemaophthalmology
2019-01-26abs ↗pdf ↗
Novel quantitative indicators of digital ophthalmoscopy image qualitycs.CV

This paper evaluates features for assessing digital ophthalmoscopy image quality.

problem Accurate teleophthalmology requires high-quality ophthalmoscopic imagery.
method Statistical metrics, gradient-based metrics, and wavelet transform coefficient derived indicators were tested using machine learning.
result Suitability of features for image quality assessment confirmed, though on a small data set.
teleophthalmologyophthalmoscopyimage qualitydiabetic retinopathymachine learning
2019-03-07abs ↗pdf ↗
On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectorscs.LG

Deep learning skin lesion classifier explained using CAVs.

problem Limited acceptance of deep learning CAD systems due to opaque decision-making.
method Mapped human understandable concepts to RECOD model using CAVs.
result Classifier learns and encodes disease-related concepts in its latent representation.
Deep LearningSkin LesionsInterpretabilityConcept Activation VectorsMedical Image Classification
2020-05-05abs ↗pdf ↗
CeCNN: Copula-enhanced convolutional neural networks in joint prediction of refraction error and axial length based on ultra-widefield fundus imagescs.CV

CeCNN predicts SE and AL from UWF images, improving myopia screening.

problem Predicting axial length and spherical equivalence from UWF fundus images.
method Copula-enhanced Convolutional Neural Network (CeCNN) for multiresponse regression.
result CeCNN improves prediction of SE and AL compared to baseline CNNs.
myopia screeningUWF fundus imagesCopulaConvolutional Neural Networkaxial length
2023-11-07abs ↗pdf ↗