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 226–250 of 424 papers

TitleStatusHype
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