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 151–175 of 424 papers

TitleStatusHype
A Fully Convolutional Normalization Approach of Head and Neck Cancer Outcome Prediction—0
Forward-Forward Contrastive Learning—0
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification—0
Class-Specific Distribution Alignment for Semi-Supervised Medical Image Classification—0
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges—0
Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning—0
Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System—0
A review of deep learning-based information fusion techniques for multimodal medical image classification—0
Adversarial Attacks and Defences for Skin Cancer Classification—0
Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition—0
Expert-Like Reparameterization of Heterogeneous Pyramid Receptive Fields in Efficient CNNs for Fair Medical Image Classification—0
Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classification—0
Applications of Sequential Learning for Medical Image Classification—0
EVM-Fusion: An Explainable Vision Mamba Architecture with Neural Algorithmic Fusion—0
Evaluating the Fairness of Neural Collapse in Medical Image Classification—0
Chest X-rays Classification: A Multi-Label and Fine-Grained Problem—0
Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization—0
Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-training—0
Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning—0
Exploring the Transferability of a Foundation Model for Fundus Images: Application to Hypertensive Retinopathy—0
Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification—0
A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis—0
Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels—0
Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study—0
FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification—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