AIMC Topic: Radiology

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The deep radiomic analytics pipeline.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Radiomics refers to the process of extracting useful imaging features from radiological data. Conventional radiomics like standard uptake value, intensity histograms, or phase images involve hand-crafted (manual) or automated regions of interest (com...

Artificial intelligence for multimodal data integration in oncology.

Cancer cell
In oncology, the patient state is characterized by a whole spectrum of modalities, ranging from radiology, histology, and genomics to electronic health records. Current artificial intelligence (AI) models operate mainly in the realm of a single modal...

Key concepts, common pitfalls, and best practices in artificial intelligence and machine learning: focus on radiomics.

Diagnostic and interventional radiology (Ankara, Turkey)
Artificial intelligence (AI) and machine learning (ML) are increasingly used in radiology research to deal with large and complex imaging data sets. Nowadays, ML tools have become easily accessible to anyone. Such a low threshold to accessibility mig...

White Matter Lesion Segmentation for Multiple Sclerosis Patients implementing deep learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The aim of this work is to address the problem of White Matter Lesion (WML) segmentation employing Magnetic Resonance Imaging (MRI) images from Multiple Sclerosis (MS) patients through the application of deep learning. A U-net based architecture cont...

Data Federation in Healthcare for Artificial Intelligence Solutions.

Studies in health technology and informatics
Data federation offers a way to get data moving from multiple sources providing advantages in healthcare systems where medical data is often hard to reach because of regulations or the lack of reliable solutions that can integrate on top of protocols...

Analysis of Causal Relationships in Integrated Ontologies of Diseases, Phenotypes, and Radiological Diagnosis.

Studies in health technology and informatics
Biomedical ontologies encode knowledge in a form that makes it computable. The current study used the integration of three large biomedical ontologies-the Disease Ontology (DO), Human Phenotype Ontology (HPO), and Radiology Gamuts Ontology (RGO)-to e...

Clinical Comparable Corpus Describing the Same Subjects with Different Expressions.

Studies in health technology and informatics
Medical artificial intelligence (AI) systems need to learn to recognize synonyms or paraphrases describing the same anatomy, disease, treatment, etc. to better understand real-world clinical documents. Existing linguistic resources focus on variants ...

Causal Associations Among Diseases and Imaging Findings in Radiology Reports.

Studies in health technology and informatics
This study explored the ability to identify causal relationships between diseases and imaging findings from their co-occurrences in radiology reports. A natural language processing (NLP) system with negative-expression filtering detected positive men...

Scaling AI Projects for Radiology - Causes and Consequences.

Studies in health technology and informatics
Artificial intelligence (AI) for radiology has the potential to handle an ever-increasing volume of imaging examinations. However, the implementation of AI for clinical practice has not lived up to expectations. We suggest that a key problem with AI ...

A "Bumper-Car" Curriculum for Teaching Deep Learning to Radiology Residents.

Academic radiology
RATIONALE AND OBJECTIVES: Our goal was to create an artificial intelligence (AI) training curriculum for residents that taught them to create, train, evaluate and refine deep learning (DL) models. Hands-on training of models was emphasized and didact...