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 251–275 of 424 papers

TitleStatusHype
Learning multiple non-mutually-exclusive tasks for improved classification of inherently ordered labels—0
A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis—0
Text-guided Foundation Model Adaptation for Long-Tailed Medical Image Classification—0
Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition—0
Texture features in medical image analysis: a survey—0
Chest X-rays Classification: A Multi-Label and Fine-Grained Problem—0
A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study—0
Cervical Cancer Detection Using Multi-Branch Deep Learning Model—0
CEC-CNN: A Consecutive Expansion-Contraction Convolutional Network for Very Small Resolution Medical Image Classification—0
MedFocusCLIP : Improving few shot classification in medical datasets using pixel wise attention—0
Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things—0
Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data—0
CBVLM: Training-free Explainable Concept-based Large Vision Language Models for Medical Image Classification—0
The GraphNet Zoo: An All-in-One Graph Based Deep Semi-Supervised Framework for Medical Image Classification—0
Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification—0
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI—0
Medi-CAT: Contrastive Adversarial Training for Medical Image Classification—0
Med-IC: Fusing a Single Layer Involution with Convolutions for Enhanced Medical Image Classification and Segmentation—0
MedKAN: An Advanced Kolmogorov-Arnold Network for Medical Image Classification—0
CAIPI in Practice: Towards Explainable Interactive Medical Image Classification—0
Brain Storm Optimization Based Swarm Learning for Diabetic Retinopathy Image Classification—0
MED-TEX: Transferring and Explaining Knowledge with Less Data from Pretrained Medical Imaging Models—0
The Importance of Background Information for Out of Distribution Generalization—0
Boosting Medical Image Classification with Segmentation Foundation Model—0
Boosting for Bounding the Worst-class Error—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