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 351–375 of 424 papers

TitleStatusHype
Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification—0
Dual-View Pyramid Pooling in Deep Neural Networks for Improved Medical Image Classification and Confidence Calibration—0
Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training—0
Unlearning Spurious Correlations in Chest X-ray Classification—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
Deep reinforced active learning for multi-class image classification—0
Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classification—0
Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification—0
Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability—0
Detecção da Psoríase Utilizando Visão Computacional: Uma Abordagem Comparativa Entre CNNs e Vision Transformers—0
Aggregative Self-Supervised Feature Learning from a Limited Sample—0
A Fully Convolutional Normalization Approach of Head and Neck Cancer Outcome Prediction—0
Development of CNN Architectures using Transfer Learning Methods for Medical Image Classification—0
Diagnosis and Analysis of Celiac Disease and Environmental Enteropathy on Biopsy Images using Deep Learning Approaches—0
Diff3Dformer: Leveraging Slice Sequence Diffusion for Enhanced 3D CT Classification with Transformer Networks—0
Unsupervised Deep Transfer Feature Learning for Medical Image Classification—0
Deep learning pipeline for image classification on mobile phones—0
Diffusion models applied to skin and oral cancer classification—0
DINO-CXR: A self supervised method based on vision transformer for chest X-ray classification—0
Distant Domain Transfer Learning for Medical Imaging—0
Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detection—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