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 101–125 of 424 papers

TitleStatusHype
Recent Advances in Medical Image Classification—0
Deep Modeling and Optimization of Medical Image ClassificationCode0
Advancements in Medical Image Classification through Fine-Tuning Natural Domain Foundation ModelsCode0
EVM-Fusion: An Explainable Vision Mamba Architecture with Neural Algorithmic Fusion—0
Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior—0
Expert-Like Reparameterization of Heterogeneous Pyramid Receptive Fields in Efficient CNNs for Fair Medical Image Classification—0
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output CodesCode0
Adapting a Segmentation Foundation Model for Medical Image Classification—0
V-EfficientNets: Vector-Valued Efficiently Scaled Convolutional Neural Network ModelsCode0
CLOG-CD: Curriculum Learning based on Oscillating Granularity of Class Decomposed Medical Image Classification—0
A Deep Bayesian Convolutional Spiking Neural Network-based CAD system with Uncertainty Quantification for Medical Images Classification—0
Embedding Radiomics into Vision Transformers for Multimodal Medical Image Classification—0
A Hybrid Fully Convolutional CNN-Transformer Model for Inherently Interpretable Medical Image Classification—0
Federated Learning for Medical Image Classification: A Comprehensive Benchmark—0
Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability—0
Diffusion models applied to skin and oral cancer classification—0
Keeping Representation Similarity in Finetuning for Medical Image Analysis—0
AI-Augmented Thyroid Scintigraphy for Robust Classification—0
MedKAN: An Advanced Kolmogorov-Arnold Network for Medical Image Classification—0
Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?Code0
Benchmarking MedMNIST dataset on real quantum hardware—0
Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification—0
Long-tailed Medical Diagnosis with Relation-aware Representation Learning and Iterative Classifier CalibrationCode0
Hybrid Deep Learning Framework for Classification of Kidney CT Images: Diagnosis of Stones, Cysts, and Tumors—0
CoRPA: Adversarial Image Generation for Chest X-rays Using Concept Vector Perturbations and Generative Models—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