Deep learning augments liver stiffness classification in children
نویسندگان
چکیده
منابع مشابه
Gender Classification with Deep Learning
For our project, we consider the task of classifying the gender of an author of a blog, novel, tweet, post or comment. Previous attempts have considered traditional NLP models such as bag of words and n-grams to capture gender differences in authorship, and apply it to a specific media (e.g. formal writing, books, tweets, or blogs). Our project takes a novel approach by applying deep learning m...
متن کاملDeep Learning in Label-free Cell Classification.
Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low...
متن کاملTransient Elastography-Based Liver Stiffness Age-Dependently Increases in Children
BACKGROUND AND AIMS Pediatric use of liver transient elastography (TE) is attractive for its non-invasiveness, but reference values have not been established. We aimed to determine reference values for TE in children. METHODS In pediatric patients (1 to 18 years), TE (FibroScan®) with an M probe was used for both liver stiffness measurement (LSM) and measurement of hepatic fat deposition by u...
متن کاملDeep Unsupervised Domain Adaptation for Image Classification via Low Rank Representation Learning
Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep ...
متن کاملClassification of Chest Radiology Images in Order to Identify Patients with COVID-19 Using Deep Learning Techniques
Background and Aim: Due to the important role of radiological images for identifying patients with COVID-19, creating a model based on deep learning methods was the main objective of this study. Materials and Methods: 15,153 available chest images of normal, COVID-19, and pneumonia individuals which were in the Kaggle data repository was used as dataset of this research. Data preprocessing inc...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pediatric Radiology
سال: 2021
ISSN: 0301-0449,1432-1998
DOI: 10.1007/s00247-020-04916-6