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 326–350 of 424 papers

TitleStatusHype
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment—0
Privacy-preserving Machine Learning for Medical Image Classification—0
Privacy-Preserving Medical Image Classification through Deep Learning and Matrix Decomposition—0
4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification—0
Understanding Calibration of Deep Neural Networks for Medical Image Classification—0
Recent Advances in Medical Image Classification—0
A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification—0
A Novel Automated Classification and Segmentation for COVID-19 using 3D CT Scans—0
RadTex: Learning Efficient Radiograph Representations from Text Reports—0
Reconstructing Images of Two Adjacent Objects through Scattering Medium Using Generative Adversarial Network—0
Visual Prompt Engineering for Medical Vision Language Models in Radiology—0
AI-Augmented Thyroid Scintigraphy for Robust Classification—0
A novel adversarial learning strategy for medical image classification—0
AnoMalNet: Outlier Detection based Malaria Cell Image Classification Method Leveraging Deep Autoencoder—0
A New Perspective to Boost Vision Transformer for Medical Image Classification—0
An ensemble framework approach of hybrid Quantum convolutional neural networks for classification of breast cancer images—0
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST—0
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification—0
Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification—0
Review of AlexNet for Medical Image Classification—0
A Test Statistic Estimation-based Approach for Establishing Self-interpretable CNN-based Binary Classifiers—0
Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models—0
A Lightweight Neural Architecture Search Model for Medical Image Classification—0
A Hybrid Fully Convolutional CNN-Transformer Model for Inherently Interpretable 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