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 5175 of 424 papers

TitleStatusHype
DiffMIC: Dual-Guidance Diffusion Network for Medical Image ClassificationCode1
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
Evolutionary Neural AutoML for Deep LearningCode1
Deep Multimodal Guidance for Medical Image ClassificationCode1
CheXFusion: Effective Fusion of Multi-View Features using Transformers for Long-Tailed Chest X-Ray ClassificationCode1
M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models and Latent Space Geometry OptimizationCode1
MONICA: Benchmarking on Long-tailed Medical Image ClassificationCode1
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive LearningCode1
BiasPruner: Debiased Continual Learning for Medical Image ClassificationCode1
Big Self-Supervised Models Advance Medical Image ClassificationCode1
PatchDropout: Economizing Vision Transformers Using Patch DropoutCode1
PHG-Net: Persistent Homology Guided Medical Image ClassificationCode1
Counterfactual Explanations for Medical Image Classification and Regression using Diffusion AutoencoderCode1
Contrastive Learning of Medical Visual Representations from Paired Images and TextCode1
Densely Connected Convolutional NetworksCode1
Explainable Deep Learning Methods in Medical Image Classification: A SurveyCode1
Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image ClassificationCode1
CASS: Cross Architectural Self-Supervision for Medical Image AnalysisCode1
Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image ClassificationCode1
Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural ImagesCode1
Systematic comparison of semi-supervised and self-supervised learning for medical image classificationCode1
CheXWorld: Exploring Image World Modeling for Radiograph Representation LearningCode1
Failure Detection in Medical Image Classification: A Reality Check and Benchmarking TestbedCode1
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State MatchingCode1
Joint Learning of Localized Representations from Medical Images and ReportsCode1
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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