Vascular Anomalies: Nomenclature, Classification, and Imaging Algorithms
ACTA RADIOLOGICA(2023)
All India Inst Med Sci
Abstract
There is a lot of ambiguity in the usage of correct terminology in the description of vascular malformations and tumors. Hemangioma and arteriovenous malformation (AVM) are the most commonly used terms and are the mostly incorrectly used as well! The aim of this review article was to lay out the correct nomenclature and describe the correct usage for the physicians and radiologists involved in diagnosing and managing these lesions. We describe the various classification systems which have been devised to define the multiple entities included under vascular anomalies. The latest classification system that should be adhered to is per the International Society for the Study of Vascular Anomalies, approved at the 20th ISSVA Workshop held in Melbourne in April 2014, last revised in May 2018. The main features of the latest revision have been highlighted. This classification, however, does not list the diagnostic clinico-radiological features for each entity. In addition, guidelines regarding the appropriate use of available imaging modalities are lacking in the literature. We, hereby, aim to address these pertinent issues in this review article.
MoreTranslated text
Key words
Vascular anomalies,classification,nomenclature,magnetic resonance imaging,ultrasound,computed tomography
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined