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 276–300 of 424 papers

TitleStatusHype
Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels—0
Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution—0
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification—0
Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification—0
MIC: Medical Image Classification Using Chest X-ray (COVID-19 and Pneumonia) Dataset with the Help of CNN and Customized CNN—0
The Whole Pathological Slide Classification via Weakly Supervised Learning—0
MixModule: Mixed CNN Kernel Module for Medical Image Segmentation—0
Mix-up Self-Supervised Learning for Contrast-agnostic Applications—0
Modality-bridge Transfer Learning for Medical Image Classification—0
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations—0
Benchmarking MedMNIST dataset on real quantum hardware—0
More for Less: Compact Convolutional Transformers Enable Robust Medical Image Classification with Limited Data—0
TOP-GAN: Label-Free Cancer Cell Classification Using Deep Learning with a Small Training Set—0
MoVL:Exploring Fusion Strategies for the Domain-Adaptive Application of Pretrained Models in Medical Imaging Tasks—0
Multi-branch CNN and grouping cascade attention for medical image classification—0
Multiclass Alignment of Confidence and Certainty for Network Calibration—0
Multi-Instance Learning by Utilizing Structural Relationship among Instances—0
Multi-Instance Multi-Scale CNN for Medical Image Classification—0
A systematic review of transfer learning based approaches for diabetic retinopathy detection—0
​4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
Multi-Sample ζ-mixup: Richer, More Realistic Synthetic Samples from a p-Series Interpolant—0
Mutual Attention-based Hybrid Dimensional Network for Multimodal Imaging Computer-aided Diagnosis—0
Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging—0
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge—0
Vision Transformers in Medical Imaging: A Review—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