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
Astroformer: More Data Might not be all you need for ClassificationCode1
DiffMIC: Dual-Guidance Diffusion Network for Medical Image ClassificationCode1
Uncertainty-informed Mutual Learning for Joint Medical Image Classification and SegmentationCode1
Pretrained ViTs Yield Versatile Representations For Medical ImagesCode1
Standardized Medical Image Classification across Medical DisciplinesCode1
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive LearningCode1
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark StudyCode1
PatchDropout: Economizing Vision Transformers Using Patch DropoutCode1
Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image ClassificationCode1
Regression Metric Loss: Learning a Semantic Representation Space for Medical ImagesCode1
Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution ShiftCode1
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image ClassificationCode1
Evaluating histopathology transfer learning with ChampKitCode1
CASS: Cross Architectural Self-Supervision for Medical Image AnalysisCode1
Failure Detection in Medical Image Classification: A Reality Check and Benchmarking TestbedCode1
Explainable Deep Learning Methods in Medical Image Classification: A SurveyCode1
A survey on attention mechanisms for medical applications: are we moving towards better algorithms?Code1
Deep Multimodal Guidance for Medical Image ClassificationCode1
Self Pre-training with Masked Autoencoders for Medical Image Classification and SegmentationCode1
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural NetworksCode1
HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical ImagesCode1
UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality BarrierCode1
Joint Learning of Localized Representations from Medical Images and ReportsCode1
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image ClassificationCode1
Malaria Parasite Detection using Efficient Neural EnsemblesCode1
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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