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 1–25 of 424 papers

TitleStatusHype
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise—0
Robust Training with Data Augmentation for Medical Imaging Classification—0
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach—0
Detecção da Psoríase Utilizando Visão Computacional: Uma Abordagem Comparativa Entre CNNs e Vision Transformers—0
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
Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior—0
EVM-Fusion: An Explainable Vision Mamba Architecture with Neural Algorithmic Fusion—0
Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image ClassificationCode1
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
CheXWorld: Exploring Image World Modeling for Radiograph Representation LearningCode1
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
Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability—0
Federated Learning for Medical Image Classification: A Comprehensive Benchmark—0
Diffusion models applied to skin and oral cancer classification—0
Fair Federated Medical Image Classification Against Quality Shift via Inter-Client Progressive State MatchingCode1
Keeping Representation Similarity in Finetuning for Medical Image Analysis—0
XFMamba: Cross-Fusion Mamba for Multi-View Medical Image ClassificationCode1
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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