SOTAVerified

Pneumonia Detection

Papers

Showing 110 of 63 papers

TitleStatusHype
Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized DataCode1
BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairsCode1
CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimizationCode1
Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer ApproachCode1
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray ImagesCode1
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive LearningCode1
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep LearningCode1
COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep LearningCode1
Deep Learning for Automatic Pneumonia DetectionCode1
Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image ClassificationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NSGANetV1-A3AUROC0.85Unverified
2NSGANetV1-XAUROC0.85Unverified
3CheXNetAUROC0.84Unverified
4MUXNet-mAUROC0.84Unverified
5AE-CNNAUROC0.82Unverified
#ModelMetricClaimedVerifiedStatus
1SelfsynthxAccuracy98.72Unverified
2DINO-CXRAccuracy95.65Unverified
3MSTPAccuracy92.79Unverified
#ModelMetricClaimedVerifiedStatus
1COVID-CXNetF-Score0.85Unverified