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 226–250 of 424 papers

TitleStatusHype
Freeze the backbones: A Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-training—0
Self-supervised learning for skin cancer diagnosis with limited training dataCode0
Sample selection with noise rate estimation in noise learning of medical image analysis—0
Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things—0
A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image ClassificationCode0
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual ClassificationCode0
Review of AlexNet for Medical Image Classification—0
Connecting the Dots: Graph Neural Network Powered Ensemble and Classification of Medical ImagesCode0
Medi-CAT: Contrastive Adversarial Training for Medical Image Classification—0
GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset—0
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge—0
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles—0
Boosting for Bounding the Worst-class Error—0
Plug-and-Play Feature Generation for Few-Shot Medical Image Classification—0
Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models—0
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI ModelsCode0
Reconstruction of Patient-Specific Confounders in AI-based Radiologic Image Interpretation using Generative PretrainingCode0
Applications of Sequential Learning for Medical Image Classification—0
Understanding Calibration of Deep Neural Networks for Medical Image Classification—0
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI—0
RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification—0
Multiclass Alignment of Confidence and Certainty for Network Calibration—0
Privacy-Preserving Medical Image Classification through Deep Learning and Matrix Decomposition—0
Concept Bottleneck with Visual Concept Filtering for Explainable Medical Image Classification—0
Robustness Stress Testing in Medical Image ClassificationCode0
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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