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Found 6 papers, 0 papers with code
MEDBind: Unifying Language and Multimodal Medical Data Embeddings
Medical vision-language pretraining models (VLPM) have achieved remarkable progress in fusing chest X-rays (CXR) with clinical texts, introducing image-text data binding approaches that enable zero-shot learning and downstream clinical tasks.
Cross-Task Attention Network: Improving Multi-Task Learning for Medical Imaging Applications
Multi-task learning (MTL) is a powerful approach in deep learning that leverages the information from multiple tasks during training to improve model performance.
Non-invasive Liver Fibrosis Screening on CT Images using Radiomics
The combination of hyperparameters and features that yielded the highest AUC was a logistic regression model with inputs features of maximum, energy, kurtosis, skewness, and small area high gray level emphasis extracted from non-contrast enhanced NC CT normalized using Gamma correction with $\gamma$ = 1. 5 (AUC, 0. 7833; 95% CI: 0. 7821, 0. 7845), (sensitivity, 0. 9091; 95% CI: 0. 9091, 0. 9091).
Domain Adaptation of Automated Treatment Planning from Computed Tomography to Magnetic Resonance
Significance: We were able to create highly acceptable MR based treatment plans using a CT-trained ML model for treatment planning, although clinically significant dose deviations from the CT based plans were observed.
A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data
We have conducted an institutional machine learning challenge to develop an accurate model for overall survival prediction in head and neck cancer using clinical data etxracted from electronic medical records and pre-treatment radiological images, as well as to evaluate the true added benefit of radiomics for head and neck cancer prognosis.
The importance of transparency and reproducibility in artificial intelligence research
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening.
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