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 251–300 of 424 papers

TitleStatusHype
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
MED-TEX: Transferring and Explaining Knowledge with Less Data from Pretrained Medical Imaging Models—0
Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels—0
Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution—0
MIC: Medical Image Classification Using Chest X-ray (COVID-19 and Pneumonia) Dataset with the Help of CNN and Customized CNN—0
MixModule: Mixed CNN Kernel Module for Medical Image Segmentation—0
Mix-up Self-Supervised Learning for Contrast-agnostic Applications—0
Modality-bridge Transfer Learning for Medical Image Classification—0
More for Less: Compact Convolutional Transformers Enable Robust Medical Image Classification with Limited Data—0
MoVL:Exploring Fusion Strategies for the Domain-Adaptive Application of Pretrained Models in Medical Imaging Tasks—0
Multi-branch CNN and grouping cascade attention for medical image classification—0
Multiclass Alignment of Confidence and Certainty for Network Calibration—0
Multi-Instance Learning by Utilizing Structural Relationship among Instances—0
Multi-Instance Multi-Scale CNN for Medical Image Classification—0
Multi-Sample ζ-mixup: Richer, More Realistic Synthetic Samples from a p-Series Interpolant—0
Mutual Attention-based Hybrid Dimensional Network for Multimodal Imaging Computer-aided Diagnosis—0
Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging—0
On evaluating CNN representations for low resource medical image classification—0
Only My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models—0
OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms—0
Optimizing Federated Learning for Medical Image Classification on Distributed Non-iid Datasets with Partial Labels—0
ParseCaps: An Interpretable Parsing Capsule Network for Medical Image Diagnosis—0
PCCT: Progressive Class-Center Triplet Loss for Imbalanced Medical Image Classification—0
Plug-and-Play Feature Generation for Few-Shot Medical Image Classification—0
PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification—0
Point-of-Care Diabetic Retinopathy Diagnosis: A Standalone Mobile Application Approach—0
Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning—0
Predictive uncertainty estimation in deep learning for lung carcinoma classification in digital pathology under real dataset shifts—0
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment—0
Privacy-preserving Machine Learning for Medical Image Classification—0
Privacy-Preserving Medical Image Classification through Deep Learning and Matrix Decomposition—0
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification—0
RadTex: Learning Efficient Radiograph Representations from Text Reports—0
Reconstructing Images of Two Adjacent Objects through Scattering Medium Using Generative Adversarial Network—0
Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST—0
Review of AlexNet for Medical Image Classification—0
A Test Statistic Estimation-based Approach for Establishing Self-interpretable CNN-based Binary Classifiers—0
Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models—0
Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification—0
Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training—0
Robust training of recurrent neural networks to handle missing data for disease progression modeling—0
Robust Training with Data Augmentation for Medical Imaging Classification—0
RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification—0
SAG-GAN: Semi-Supervised Attention-Guided GANs for Data Augmentation on Medical Images—0
Sample selection with noise rate estimation in noise learning of medical image analysis—0
Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability—0
Semi-supervised learning for medical image classification using imbalanced training data—0
Simple black-box universal adversarial attacks on medical image classification based on deep neural networks—0
Spatio-Temporal Structure Consistency for Semi-supervised 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