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 125 of 424 papers

TitleStatusHype
MONAI: An open-source framework for deep learning in healthcareCode5
RegNet: Self-Regulated Network for Image ClassificationCode4
Deep Residual Learning for Image RecognitionCode4
MedMamba: Vision Mamba for Medical Image ClassificationCode4
EfficientNet: Rethinking Model Scaling for Convolutional Neural NetworksCode3
DaViT: Dual Attention Vision TransformersCode2
MedViT: A Robust Vision Transformer for Generalized Medical Image ClassificationCode2
HiFuse: Hierarchical Multi-Scale Feature Fusion Network for Medical Image ClassificationCode2
MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image ClassificationCode2
Medical Image Classification with KAN-Integrated Transformers and Dilated Neighborhood AttentionCode2
Deep Multimodal Guidance for Medical Image ClassificationCode1
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image ClassificationCode1
Contrastive Learning of Medical Visual Representations from Paired Images and TextCode1
A high-order focus interaction model and oral ulcer dataset for oral ulcer segmentationCode1
CheXWorld: Exploring Image World Modeling for Radiograph Representation LearningCode1
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label CorrectionCode1
CASS: Cross Architectural Self-Supervision for Medical Image AnalysisCode1
Balanced-MixUp for Highly Imbalanced Medical Image ClassificationCode1
Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical ImagesCode1
BiasPruner: Debiased Continual Learning for Medical Image ClassificationCode1
Achieving Fairness Through Channel Pruning for Dermatological Disease DiagnosisCode1
Big Self-Supervised Models Advance Medical Image ClassificationCode1
Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image ClassificationCode1
Boosting Memory Efficiency in Transfer Learning for High-Resolution Medical Image ClassificationCode1
A Single Graph Convolution Is All You Need: Efficient Grayscale Image ClassificationCode1
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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