Retinal OCT Disease Classification

8 papers with code • 2 benchmarks • 1 datasets

Classifying different Retinal degeneration from Optical Coherence Tomography Images (OCT).


Use these libraries to find Retinal OCT Disease Classification models and implementations
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Most implemented papers

Deep Residual Learning for Image Recognition

tensorflow/models CVPR 2016

Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

MobileNetV2: Inverted Residuals and Linear Bottlenecks

tensorflow/models CVPR 2018

In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes.

Rethinking the Inception Architecture for Computer Vision

tensorflow/models CVPR 2016

Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks.

Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images

SharifAmit/OCT_Classification 13 Oct 2019

Diagnosing different retinal diseases from Spectral Domain Optical Coherence Tomography (SD-OCT) images is a challenging task.

Retinal OCT disease classification with variational autoencoder regularization

mlaves/oct-classification-vae-regularization 23 Mar 2019

A recent study established a diagnostic tool based on convolutional neural networks (CNN), which was trained on a large database of retinal OCT images.

Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images

SharifAmit/Robust_Joint_Attention 16 May 2020

Noisy data and the similarity in the ocular appearances caused by different ophthalmic pathologies pose significant challenges for an automated expert system to accurately detect retinal diseases.

Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels

Valentyn1997/oct-diagn-semi-supervised 23 Oct 2020

Furthermore, our experiments show that exponential moving average (EMA) of model parameters, which is a component of both algorithms, is not needed for our classification problem, as disabling it leaves the outcome unchanged.

TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes

EmreTaha/TINC 30 Jun 2022

Recent contrastive learning methods achieved state-of-the-art in low label regimes.