Retinal OCT Disease Classification
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Benchmarks
OCT2017
Srinivasan2014
Most implemented
Deep Residual Learning for Image Recognition
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Rethinking the Inception Architecture for Computer Vision
Optic-Net: A Novel Convolutional Neural Network for Diagnosis of Retinal Diseases from Optical Tomography Images
Papers
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 Classification