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
Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-training—0
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?—0
Prompt-driven Latent Domain Generalization for Medical Image ClassificationCode1
Freeze the backbones: A Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-training—0
Self-supervised learning for skin cancer diagnosis with limited training dataCode0
Sample selection with noise rate estimation in noise learning of medical image analysis—0
Building Universal Foundation Models for Medical Image Analysis with Spatially Adaptive NetworksCode1
Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things—0
PHG-Net: Persistent Homology Guided Medical Image ClassificationCode1
Only Positive Cases: 5-fold High-order Attention Interaction Model for Skin Segmentation Derived ClassificationCode1
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual ClassificationCode0
A Parameterized Generative Adversarial Network Using Cyclic Projection for Explainable Medical Image ClassificationCode0
Review of AlexNet for Medical Image Classification—0
Connecting the Dots: Graph Neural Network Powered Ensemble and Classification of Medical ImagesCode0
Medi-CAT: Contrastive Adversarial Training for Medical Image Classification—0
Are Natural Domain Foundation Models Useful for Medical Image Classification?Code1
GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset—0
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge—0
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles—0
Interpretable Medical Image Classification using Prototype Learning and Privileged InformationCode1
Boosting for Bounding the Worst-class Error—0
Plug-and-Play Feature Generation for Few-Shot Medical Image Classification—0
Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models—0
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI ModelsCode0
Reconstruction of Patient-Specific Confounders in AI-based Radiologic Image Interpretation using Generative PretrainingCode0
fastMONAI: A low-code deep learning library for medical image analysisCode1
Applications of Sequential Learning for Medical Image Classification—0
Understanding Calibration of Deep Neural Networks for Medical Image Classification—0
Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI—0
RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification—0
Multiclass Alignment of Confidence and Certainty for Network Calibration—0
Privacy-Preserving Medical Image Classification through Deep Learning and Matrix Decomposition—0
Concept Bottleneck with Visual Concept Filtering for Explainable Medical Image Classification—0
How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?Code1
Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural ImagesCode1
Robustness Stress Testing in Medical Image ClassificationCode0
Distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitations—0
Robust Asymmetric Loss for Multi-Label Long-Tailed LearningCode1
Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image ClassificationCode1
Few-shot medical image classification with simple shape and texture text descriptors using vision-language models—0
Improving Medical Image Classification in Noisy Labels Using Only Self-supervised PretrainingCode1
CheXFusion: Effective Fusion of Multi-View Features using Transformers for Long-Tailed Chest X-Ray ClassificationCode1
Unlearning Spurious Correlations in Chest X-ray Classification—0
DINO-CXR: A self supervised method based on vision transformer for chest X-ray classification—0
L3DMC: Lifelong Learning using Distillation via Mixed-Curvature SpaceCode0
Class-Specific Distribution Alignment for Semi-Supervised Medical Image Classification—0
Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image ClassificationCode1
Understanding Silent Failures in Medical Image ClassificationCode1
Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification—0
Interpreting and Correcting Medical Image Classification with PIP-NetCode1
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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