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 101–150 of 424 papers

TitleStatusHype
Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification—0
4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise—0
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations—0
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification—0
Recent Advances in Medical Image Classification—0
Benchmarking MedMNIST dataset on real quantum hardware—0
Data Augmentation using Feature Generation for Volumetric Medical Images—0
Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise—0
Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification—0
Active Globally Explainable Learning for Medical Images via Class Association Embedding and Cyclic Adversarial Generation—0
Federated Learning for Medical Image Classification: A Comprehensive Benchmark—0
Cross-Modal Information Maximization for Medical Imaging: CMIM—0
Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities—0
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning—0
Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data—0
Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images—0
Deep learning pipeline for image classification on mobile phones—0
Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks—0
A Lightweight Neural Architecture Search Model for Medical Image Classification—0
CoRPA: Adversarial Image Generation for Chest X-rays Using Concept Vector Perturbations and Generative Models—0
FairREAD: Re-fusing Demographic Attributes after Disentanglement for Fair Medical Image Classification—0
CopilotCAD: Empowering Radiologists with Report Completion Models and Quantitative Evidence from Medical Image Foundation Models—0
Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection—0
A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000—0
Contrastive Centroid Supervision Alleviates Domain Shift in Medical Image Classification—0
FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis—0
Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior—0
A systematic review of transfer learning based approaches for diabetic retinopathy detection—0
Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification—0
CONFINE: Conformal Prediction for Interpretable Neural Networks—0
Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration—0
Concept Bottleneck with Visual Concept Filtering for Explainable Medical Image Classification—0
A Hybrid Fully Convolutional CNN-Transformer Model for Inherently Interpretable Medical Image Classification—0
Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward—0
Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization—0
Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification—0
Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis—0
Compositional Training for End-to-End Deep AUC Maximization—0
Comparison of fine-tuning strategies for transfer learning in medical image classification—0
Assessing Robustness to Noise: Low-Cost Head CT Triage—0
Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification—0
Aggregative Self-Supervised Feature Learning from a Limited Sample—0
Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization—0
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification—0
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification—0
CLOG-CD: Curriculum Learning based on Oscillating Granularity of Class Decomposed Medical Image Classification—0
A Fully Convolutional Normalization Approach of Head and Neck Cancer Outcome Prediction—0
Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-training—0
CLINICAL: Targeted Active Learning for Imbalanced 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