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 201–250 of 424 papers

TitleStatusHype
Spatio-Temporal Structure Consistency for Semi-supervised Medical Image Classification—0
GenMix: Combining Generative and Mixture Data Augmentation for Medical Image Classification—0
GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset—0
SPLAL: Similarity-based pseudo-labeling with alignment loss for semi-supervised medical image classification—0
Compositional Training for End-to-End Deep AUC Maximization—0
Comparison of fine-tuning strategies for transfer learning in medical image classification—0
Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance—0
Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification—0
Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification—0
Higher Order Transformers: Efficient Attention Mechanism for Tensor Structured Data—0
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification—0
Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification—0
HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach—0
Adapting a Segmentation Foundation Model for Medical Image Classification—0
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification—0
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?—0
How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?—0
How Transferable Are Self-supervised Features in Medical Image Classification Tasks?—0
Hybrid Deep Learning Framework for Classification of Kidney CT Images: Diagnosis of Stones, Cysts, and Tumors—0
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification—0
Imbalanced Classification in Medical Imaging via Regrouping—0
Improved EATFormer: A Vision Transformer for Medical Image Classification—0
Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound—0
CLOG-CD: Curriculum Learning based on Oscillating Granularity of Class Decomposed Medical Image Classification—0
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation—0
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles—0
Improving Sample Complexity with Observational Supervision—0
Active Globally Explainable Learning for Medical Images via Class Association Embedding and Cyclic Adversarial Generation—0
InceptionCapsule: Inception-Resnet and CapsuleNet with self-attention for medical image Classification—0
In-context learning enables multimodal large language models to classify cancer pathology images—0
Information Gain Sampling for Active Learning in Medical Image Classification—0
Variational Knowledge Distillation for Disease Classification in Chest X-Rays—0
Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks—0
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction—0
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification—0
Class-Specific Distribution Alignment for Semi-Supervised Medical Image Classification—0
Invariant Scattering Transform for Medical Imaging—0
Investigating the Robustness of Vision Transformers against Label Noise in Medical Image Classification—0
Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification—0
SynthVision - Harnessing Minimal Input for Maximal Output in Computer Vision Models using Synthetic Image data—0
Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning—0
Judge Like a Real Doctor: Dual Teacher Sample Consistency Framework for Semi-supervised Medical Image Classification—0
Keeping Representation Similarity in Finetuning for Medical Image Analysis—0
A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000—0
Weakly-supervised Generative Adversarial Networks for medical image classification—0
Label-noise-tolerant medical image classification via self-attention and self-supervised learning—0
Learning and Exploiting Interclass Visual Correlations for Medical Image Classification—0
Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System—0
Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification—0
Learning from Exemplary Explanations—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