SOTAVerified

Medical Image Classification

Medical Image Classification is a task in medical image analysis that involves classifying medical images, such as X-rays, MRI scans, and CT scans, into different categories based on the type of image or the presence of specific structures or diseases. The goal is to use computer algorithms to automatically identify and classify medical images based on their content, which can help in diagnosis, treatment planning, and disease monitoring.

Papers

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Showing 351–375 of 424 papers

TitleStatusHype
Semi-supervised learning for medical image classification using imbalanced training data—0
Simple black-box universal adversarial attacks on medical image classification based on deep neural networks—0
Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound—0
Reconstructing Images of Two Adjacent Objects through Scattering Medium Using Generative Adversarial Network—0
CHEF: A Cheap and Fast Pipeline for Iteratively Cleaning Label Uncertainties (Technical Report)Code0
Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classificationCode0
Bayesian Statistics Guided Label Refurbishment Mechanism: Mitigating Label Noise in Medical Image ClassificationCode0
A systematic review of transfer learning based approaches for diabetic retinopathy detection—0
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment—0
SKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI DataCode0
Variational Knowledge Distillation for Disease Classification in Chest X-Rays—0
Transfer learning for automatic brain tumor classification Using MRI Images.—0
TransMed: Transformers Advance Multi-modal Medical Image Classification—0
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation—0
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification—0
PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party SettingCode0
Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels—0
Multi-Instance Learning by Utilizing Structural Relationship among Instances—0
DenseNet for Breast Tumor Classification in Mammographic ImagesCode0
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations—0
Aggregative Self-Supervised Feature Learning from a Limited Sample—0
Application of the Neural Network Dependability Kit in Real-World Environments—0
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification—0
Distant Domain Transfer Learning for Medical Imaging—0
SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images—0
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All datasetsNCT-CRC-HE-100KIDRiDImageNetPCOS ClassificationCheXphotoGalaxy10 DECalsISIC 2017ISIC 2020 Challenge DatasetMalaria DatasetOASIS 3

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Efficientnet-b0Accuracy (%)95.59—Unverified
2ResNeXt-50-32x4dAccuracy (%)95.46—Unverified
3RegNetY-3.2GFAccuracy (%)95.42—Unverified
4ResNet-50Accuracy (%)94.72—Unverified
5DenseNet-169Accuracy (%)94.41—Unverified
6Res2Net-50Accuracy (%)93.37—Unverified
7ResNet-18Accuracy (%)92.66—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-152Accuracy (% )86.56—Unverified
2Beta-RankAccuracy81.88—Unverified
#ModelMetricClaimedVerifiedStatus
1DaViT-SGFLOPs8.8—Unverified
2DaViT-TGFLOPs4.5—Unverified
#ModelMetricClaimedVerifiedStatus
1InceptionV31:1 Accuracy90.2—Unverified
2EfficientNet B71:1 Accuracy88.9—Unverified
#ModelMetricClaimedVerifiedStatus
1PTRNMean AUC0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1AstroformerTop-1 Accuracy (%)94.87—Unverified
#ModelMetricClaimedVerifiedStatus
1Beta-RankAccuracy72.44—Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientNet EnsembleAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1SNAPSHOT ENSEMBLEF1 score99.37—Unverified
#ModelMetricClaimedVerifiedStatus
13D CNNAUC87—Unverified