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 276–300 of 424 papers

TitleStatusHype
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image AnalysisCode0
Scale Federated Learning for Label Set Mismatch in Medical Image ClassificationCode0
Self-supervision for medical image classification: state-of-the-art performance with ~100 labeled training samples per classCode0
Universal Semi-Supervised Learning for Medical Image ClassificationCode0
A Test Statistic Estimation-based Approach for Establishing Self-interpretable CNN-based Binary Classifiers—0
Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels—0
AnoMalNet: Outlier Detection based Malaria Cell Image Classification Method Leveraging Deep Autoencoder—0
Spatio-Temporal Structure Consistency for Semi-supervised Medical Image Classification—0
Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning—0
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification—0
A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000—0
CECT: Controllable Ensemble CNN and Transformer for COVID-19 Image ClassificationCode0
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imagingCode0
Transfer Learning and Class Decomposition for Detecting the Cognitive Decline of Alzheimer Disease—0
Counterfactual Explanation and Instance-Generation using Cycle-Consistent Generative Adversarial NetworksCode0
Convolutional XGBoost (C-XGBOOST) Model for Brain Tumor Detection—0
On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal RepresentationsCode0
A New Perspective to Boost Vision Transformer for Medical Image Classification—0
PEFAT: Boosting Semi-Supervised Medical Image Classification via Pseudo-Loss Estimation and Feature Adversarial TrainingCode0
Chest X-Ray Images Classification with CNNCode0
LMFLOSS: A Hybrid Loss For Imbalanced Medical Image ClassificationCode0
Adversarial Attacks and Defences for Skin Cancer Classification—0
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification—0
Vision Transformers in Medical Imaging: A Review—0
Detecting Shortcuts in Medical Images -- A Case Study in Chest X-raysCode0
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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