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 176–200 of 424 papers

TitleStatusHype
Applications of Sequential Learning for Medical Image Classification—0
EVM-Fusion: An Explainable Vision Mamba Architecture with Neural Algorithmic Fusion—0
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification—0
Evaluating the Fairness of Neural Collapse in Medical Image Classification—0
Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis—0
Chest X-rays Classification: A Multi-Label and Fine-Grained Problem—0
A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis—0
Improved EATFormer: A Vision Transformer for Medical Image Classification—0
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise—0
Improving Sample Complexity with Observational Supervision—0
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?—0
Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study—0
Few-shot medical image classification with simple shape and texture text descriptors using vision-language models—0
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach—0
How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?—0
Application of the Neural Network Dependability Kit in Real-World Environments—0
How Transferable Are Self-supervised Features in Medical Image Classification Tasks?—0
Enhancing Image Classification in Small and Unbalanced Datasets through Synthetic Data Augmentation—0
Forward-Forward Contrastive Learning—0
Cervical Cancer Detection Using Multi-Branch Deep Learning Model—0
​4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification—0
Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification—0
Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning—0
Embeddings are all you need! Achieving High Performance Medical Image Classification through Training-Free Embedding Analysis—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