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

Showing 76100 of 424 papers

TitleStatusHype
ResNet strikes back: An improved training procedure in timmCode1
Balanced-MixUp for Highly Imbalanced Medical Image ClassificationCode1
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image ClassificationCode1
Federated Semi-supervised Medical Image Classification via Inter-client Relation MatchingCode1
Rethinking Transfer Learning for Medical Image ClassificationCode1
Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained EnvironmentsCode1
Meta-Learning with Fewer Tasks through Task InterpolationCode1
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement LearningCode1
Big Self-Supervised Models Advance Medical Image ClassificationCode1
Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image ClassificationCode1
Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image TranslationCode1
Identifying Melanoma Images using EfficientNet Ensemble: Winning Solution to the SIIM-ISIC Melanoma Classification ChallengeCode1
Contrastive Learning of Medical Visual Representations from Paired Images and TextCode1
Semi-supervised Medical Image Classification with Global Latent MixingCode1
Semi-supervised Medical Image Classification with Relation-driven Self-ensembling ModelCode1
Tensor Networks for Medical Image ClassificationCode1
Res2Net: A New Multi-scale Backbone ArchitectureCode1
Evolutionary Neural AutoML for Deep LearningCode1
Semi-Supervised Deep Learning for Abnormality Classification in Retinal ImagesCode1
Densely Connected Convolutional NetworksCode1
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise0
Robust Training with Data Augmentation for Medical Imaging Classification0
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach0
Detecção da Psoríase Utilizando Visão Computacional: Uma Abordagem Comparativa Entre CNNs e Vision Transformers0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Efficientnet-b0Accuracy (%)95.59Unverified
2ResNeXt-50-32x4dAccuracy (%)95.46Unverified
3RegNetY-3.2GFAccuracy (%)95.42Unverified
4ResNet-50Accuracy (%)94.72Unverified
5DenseNet-169Accuracy (%)94.41Unverified
6Res2Net-50Accuracy (%)93.37Unverified
7ResNet-18Accuracy (%)92.66Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-152Accuracy (% )86.56Unverified
2Beta-RankAccuracy81.88Unverified
#ModelMetricClaimedVerifiedStatus
1DaViT-SGFLOPs8.8Unverified
2DaViT-TGFLOPs4.5Unverified
#ModelMetricClaimedVerifiedStatus
1InceptionV31:1 Accuracy90.2Unverified
2EfficientNet B71:1 Accuracy88.9Unverified
#ModelMetricClaimedVerifiedStatus
1PTRNMean AUC0.85Unverified
#ModelMetricClaimedVerifiedStatus
1AstroformerTop-1 Accuracy (%)94.87Unverified
#ModelMetricClaimedVerifiedStatus
1Beta-RankAccuracy72.44Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientNet EnsembleAUC0.95Unverified
#ModelMetricClaimedVerifiedStatus
1SNAPSHOT ENSEMBLEF1 score99.37Unverified
#ModelMetricClaimedVerifiedStatus
13D CNNAUC87Unverified