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 201225 of 424 papers

TitleStatusHype
Spatio-Temporal Structure Consistency for Semi-supervised Medical Image Classification0
GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification0
GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset0
SPLAL: Similarity-based pseudo-labeling with alignment loss for semi-supervised medical image classification0
Compositional Training for End-to-End Deep AUC Maximization0
Comparison of fine-tuning strategies for transfer learning in medical image classification0
Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance0
Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification0
Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification0
Higher Order Transformers: Efficient Attention Mechanism for Tensor Structured Data0
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification0
Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification0
HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach0
Adapting a Segmentation Foundation Model for Medical Image Classification0
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification0
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?0
How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?0
How Transferable Are Self-supervised Features in Medical Image Classification Tasks?0
Hybrid Deep Learning Framework for Classification of Kidney CT Images: Diagnosis of Stones, Cysts, and Tumors0
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification0
Imbalanced Classification in Medical Imaging via Regrouping0
Improved EATFormer: A Vision Transformer for Medical Image Classification0
Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound0
CLOG-CD: Curriculum Learning based on Oscillating Granularity of Class Decomposed Medical Image Classification0
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation0
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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