Papers Retinal OCT Disease Classification
“Retinal OCT Disease Classification” 태그가 달린 논문 12편 · 필터 해제
UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection
Anomaly detection (AD) is a crucial visual task aimed at recognizing abnormal pattern within samples. However, most existing AD methods suffer from limited generalizability, as they are primarily designed for domain-spec…
Anomaly DetectionImage ClassificationMedical Image SegmentationMulti-class Anomaly Detection+1ViT-2SPN: Vision Transformer-based Dual-Stream Self-Supervised Pretraining Networks for Retinal OCT Classification
Optical Coherence Tomography (OCT) is a non-invasive imaging modality essential for diagnosing various eye diseases. Despite its clinical significance, developing OCT-based diagnostic tools faces challenges, such as limi…
Data AugmentationDiagnosticMedical Image ClassificationRetinal OCT Disease Classification+1TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes
Recent contrastive learning methods achieved state-of-the-art in low label regimes. However, the training requires large batch sizes and heavy augmentations to create multiple views of an image. With non-contrastive meth…
Contrastive LearningRetinal OCT Disease ClassificationSelf-Supervised LearningDemystifying Deep Learning Models for Retinal OCT Disease Classification using Explainable AI
In the world of medical diagnostics, the adoption of various deep learning techniques is quite common as well as effective, and its statement is equally true when it comes to implementing it into the retina Optical Coher…
Retinal OCT Disease ClassificationMatching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels
Unlabeled data is often abundant in the clinic, making machine learning methods based on semi-supervised learning a good match for this setting. Despite this, they are currently receiving relatively little attention in m…
DiagnosticMedical Image AnalysisRetinal OCT Disease ClassificationSemi-Supervised Image Classification+1Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
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. In addition, the lack o…
Retinal OCT Disease ClassificationOptic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
Diagnosing different retinal diseases from Spectral Domain Optical Coherence Tomography (SD-OCT) images is a challenging task. Different automated approaches such as image processing, machine learning and deep learning a…
Retinal OCT Disease ClassificationRetinal OCT disease classification with variational autoencoder regularization
According to the World Health Organization, 285 million people worldwide live with visual impairment. The most commonly used imaging technique for diagnosis in ophthalmology is optical coherence tomography (OCT). However…
ClassificationClusteringDiagnosticGeneral Classification+1MobileNetV2: Inverted Residuals and Linear Bottlenecks
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. We…
Image ClassificationImage SegmentationObject DetectionPerson Re-Identification+2Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration
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 …
General ClassificationRetinal OCT Disease ClassificationSpecificityDeep Residual Learning for Image Recognition
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers…
ClassificationDomain GeneralizationDynamic Facial Expression Recognition+11Rethinking the Inception Architecture for Computer Vision
Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains …
Computational EfficiencyImage ClassificationRetinal OCT Disease ClassificationRobotic Grasping