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Found 8 papers, 3 papers with code
Reshaping Free-Text Radiology Notes Into Structured Reports With Generative Transformers
We propose a pipeline to extract information from free-text radiology reports, that fits with the items of the reference SR registry proposed by a national society of interventional and medical radiology, focusing on CT staging of patients with lymphoma.
Evaluation of Predictive Reliability to Foster Trust in Artificial Intelligence. A case study in Multiple Sclerosis
We propose a simple approach that can be used in the deployment phase of any ML model to suggest whether to trust predictions or not.
Advancing Italian Biomedical Information Extraction with Transformers-based Models: Methodological Insights and Multicenter Practical Application
The introduction of computerized medical records in hospitals has reduced burdensome activities like manual writing and information fetching.
CAD-RADS scoring of coronary CT angiography with Multi-Axis Vision Transformer: a clinically-inspired deep learning pipeline
The standard non-invasive imaging technique used to assess the severity and extent of Coronary Artery Disease (CAD) is Coronary Computed Tomography Angiography (CCTA).
Localising In-Domain Adaptation of Transformer-Based Biomedical Language Models
In the era of digital healthcare, the huge volumes of textual information generated every day in hospitals constitute an essential but underused asset that could be exploited with task-specific, fine-tuned biomedical language representation models, improving patient care and management.
Tree-based local explanations of machine learning model predictions, AraucanaXAI
Increasingly complex learning methods such as boosting, bagging and deep learning have made ML models more accurate, but harder to understand and interpret.
The Gene Mover's Distance: Single-cell similarity via Optimal Transport
In the Optimal Transport model, we use two types of cost function for measuring the distance between a pair of genes.
Temporal Reasoning with Probabilities
In the first, the CPN includes variables representing ? event-occurrence times?, possibly on different time scales, and variables representing the ? state?
