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Found 6 papers, 5 papers with code
MedPromptX: Grounded Multimodal Prompting for Chest X-ray Diagnosis
Chest X-ray images are commonly used for predicting acute and chronic cardiopulmonary conditions, but efforts to integrate them with structured clinical data face challenges due to incomplete electronic health records (EHR).
Fine-Tuned Large Language Models for Symptom Recognition from Spanish Clinical Text
The accurate recognition of symptoms in clinical reports is significantly important in the fields of healthcare and biomedical natural language processing.
Improving Pseudo-labelling and Enhancing Robustness for Semi-Supervised Domain Generalization
A key challenge, faced by the best-performing SSL-based SSDG methods, is selecting accurate pseudo-labels under multiple domain shifts and reducing overfitting to source domains under limited labels.
PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism Diagnosis
Previous deep learning efforts have focused on improving the performance of Pulmonary Embolism(PE) diagnosis from Computed Tomography (CT) scans using Convolutional Neural Networks (CNN).
OptBA: Optimizing Hyperparameters with the Bees Algorithm for Improved Medical Text Classification
One of the challenges that artificial intelligence engineers face, specifically in the field of deep learning is obtaining the optimal model hyperparameters.
Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text
The increase in people's use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords.
Ranked #1 on
Ensemble Learning
on SMS Spam Collection Data Set
