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Label attention meets deep learning: a novel model for ICD-11 automatic classification of Chinese EMRs

BiLSTM, an ICD-11 automatic coding model using MC-BERT and label attention. Experiments on clinical records show 83.86% ...
Frontiers

VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data

In large public multi-site fMRI datasets, the sample characteristics, data acquisition methods, and MRI scanner models vary across sites and datasets. This non-neural variability obscures neural ...
Scientific Research Publishing

Wang, H., Feng, T., Zhao, Z., Bai, X., Han, G., Wang, J.,

ABSTRACT: Dementia is a progressive neurodegenerative disorder that significantly impacts cognitive function, with early and accurate diagnosis remaining a clinical challenge. Traditional diagnostic ...
IEEE

Multi-label Chest X-ray Image Classification via Category Disentangled Causal Learning

Abstract: Chest X-rays (CXR) are widely used to diagnose chest diseases. Since patients often suffer from multiple diseases simultaneously, it is crucial to identify multiple abnormalities in a single ...
Frontiers

BrainTumNet: multi-task deep learning framework for brain tumor segmentation and classification using adaptive masked transformers

Background and objective: Accurate diagnosis of brain tumors significantly impacts patient prognosis and treatment planning. Traditional diagnostic methods primarily rely on clinicians’ subjective ...
IEEE

Partial Multi-View Incomplete Multi-Label Learning Network With Quality-Aware Representation Fusion

Abstract: Recently, the topic of multi-view multi-label classification has aroused significant attention from scholars. Plenty of methods adopt an average weighting scheme to merge the features ...