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

Showing 301325 of 424 papers

TitleStatusHype
A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study0
Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward0
Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels0
Self Pre-training with Masked Autoencoders for Medical Image Classification and SegmentationCode1
Deep Multimodal Guidance for Medical Image ClassificationCode1
FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis0
Improving the repeatability of deep learning models with Monte Carlo dropoutCode0
An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural NetworksCode1
Detection of Dementia Through 3D Convolutional Neural Networks Based on Amyloid PETCode0
Mutual Attention-based Hybrid Dimensional Network for Multimodal Imaging Computer-aided Diagnosis0
HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical ImagesCode1
UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality BarrierCode1
Joint Learning of Localized Representations from Medical Images and ReportsCode1
Weakly-supervised Generative Adversarial Networks for medical image classification0
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image ClassificationCode1
Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification0
Deep AUC Maximization for Medical Image Classification: Challenges and Opportunities0
Malaria Parasite Detection using Efficient Neural EnsemblesCode1
FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
ResNet strikes back: An improved training procedure in timmCode1
Compositional Training for End-to-End Deep AUC Maximization0
Does deep learning model calibration improve performance in class-imbalanced medical image classification?0
Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System0
Balanced-MixUp for Highly Imbalanced Medical Image ClassificationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Efficientnet-b0Accuracy (%)95.59Unverified
2ResNeXt-50-32x4dAccuracy (%)95.46Unverified
3RegNetY-3.2GFAccuracy (%)95.42Unverified
4ResNet-50Accuracy (%)94.72Unverified
5DenseNet-169Accuracy (%)94.41Unverified
6Res2Net-50Accuracy (%)93.37Unverified
7ResNet-18Accuracy (%)92.66Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet-152Accuracy (% )86.56Unverified
2Beta-RankAccuracy81.88Unverified
#ModelMetricClaimedVerifiedStatus
1DaViT-SGFLOPs8.8Unverified
2DaViT-TGFLOPs4.5Unverified
#ModelMetricClaimedVerifiedStatus
1InceptionV31:1 Accuracy90.2Unverified
2EfficientNet B71:1 Accuracy88.9Unverified
#ModelMetricClaimedVerifiedStatus
1PTRNMean AUC0.85Unverified
#ModelMetricClaimedVerifiedStatus
1AstroformerTop-1 Accuracy (%)94.87Unverified
#ModelMetricClaimedVerifiedStatus
1Beta-RankAccuracy72.44Unverified
#ModelMetricClaimedVerifiedStatus
1EfficientNet EnsembleAUC0.95Unverified
#ModelMetricClaimedVerifiedStatus
1SNAPSHOT ENSEMBLEF1 score99.37Unverified
#ModelMetricClaimedVerifiedStatus
13D CNNAUC87Unverified