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 151–200 of 424 papers

TitleStatusHype
Aggregative Self-Supervised Feature Learning from a Limited Sample—0
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation—0
Federated Learning for Medical Image Classification: A Comprehensive Benchmark—0
Feature Preserving Shrinkage on Bayesian Neural Networks via the R2D2 Prior—0
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification—0
CLOG-CD: Curriculum Learning based on Oscillating Granularity of Class Decomposed Medical Image Classification—0
FairREAD: Re-fusing Demographic Attributes after Disentanglement for Fair Medical Image Classification—0
FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis—0
A Fully Convolutional Normalization Approach of Head and Neck Cancer Outcome Prediction—0
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles—0
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification—0
Class-Specific Distribution Alignment for Semi-Supervised Medical Image Classification—0
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges—0
Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning—0
Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning—0
Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-training—0
Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization—0
Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System—0
A review of deep learning-based information fusion techniques for multimodal medical image classification—0
Exploring the Transferability of a Foundation Model for Fundus Images: Application to Hypertensive Retinopathy—0
Exploring the Versatility of Zero-Shot CLIP for Interstitial Lung Disease Classification—0
Adversarial Attacks and Defences for Skin Cancer Classification—0
Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition—0
Expert-Like Reparameterization of Heterogeneous Pyramid Receptive Fields in Efficient CNNs for Fair Medical Image Classification—0
Evolution-aware VAriance (EVA) Coreset Selection for Medical Image Classification—0
Applications of Sequential Learning for Medical Image Classification—0
EVM-Fusion: An Explainable Vision Mamba Architecture with Neural Algorithmic Fusion—0
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification—0
Evaluating the Fairness of Neural Collapse in Medical Image Classification—0
Federated Distillation for Medical Image Classification: Towards Trustworthy Computer-Aided Diagnosis—0
Chest X-rays Classification: A Multi-Label and Fine-Grained Problem—0
A ChatGPT Aided Explainable Framework for Zero-Shot Medical Image Diagnosis—0
Improved EATFormer: A Vision Transformer for Medical Image Classification—0
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise—0
Improving Sample Complexity with Observational Supervision—0
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?—0
Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study—0
Few-shot medical image classification with simple shape and texture text descriptors using vision-language models—0
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach—0
How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?—0
Application of the Neural Network Dependability Kit in Real-World Environments—0
How Transferable Are Self-supervised Features in Medical Image Classification Tasks?—0
Enhancing Image Classification in Small and Unbalanced Datasets through Synthetic Data Augmentation—0
Forward-Forward Contrastive Learning—0
Cervical Cancer Detection Using Multi-Branch Deep Learning Model—0
​4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection—0
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification—0
Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification—0
Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning—0
Embeddings are all you need! Achieving High Performance Medical Image Classification through Training-Free Embedding Analysis—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