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Survival Prediction Model for Patients with Hepatocellular Carcinoma and Extrahepatic Metastasis Based on XGBoost Algorithm.
J Hepatocell Carcinoma
December 2023
Division of Gastroenterology and Hepatology, Department of Internal Medicine, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Purpose: Accurate estimation of survival is of utmost importance in patients with hepatocellular carcinoma (HCC) and extrahepatic metastasis. This study aimed to develop a survival prediction model using real-world data.
Patients And Methods: A total of 993 patients with treatment-naïve HCC and extrahepatic metastasis were included from 13 Korean hospitals between 2013 and 2018.
FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting.
Sensors (Basel)
November 2021
Computer Science and Information Engineering, National Yunlin University of Science and Technology, Douliu 64002, Taiwan.
The aim of this paper is to distinguish the vehicle detection and count the class number in each classification from the inputs. We proposed the use of Fuzzy Guided Scale Choice (FGSC)-based SSD deep neural network architecture for vehicle detection and class counting with parameter optimization. The 'FGSC' blocks are integrated into the convolutional layers of the model, which emphasize essential features while ignoring less important ones that are not significant for the operation.
View Article and Find Full Text PDFEvaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs.
SN Comput Sci
June 2021
School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, 1 Jan Smuts Avenue, Braamfontein South Africa.
Chest X-rays are a vital diagnostic tool in the workup of many patients. Similar to most medical imaging modalities, they are profoundly multi-modal and are capable of visualising a variety of combinations of conditions. There is an ever pressing need for greater quantities of labelled images to drive forward the development of diagnostic tools; however, this is in direct opposition to concerns regarding patient confidentiality which constrains access through permission requests and ethics approvals.
View Article and Find Full Text PDFExceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy.
J Neurosci Methods
July 2015
DYCOG Lab, Lyon Neuroscience Research Center, INSERM U1028, UMR 5292, University Lyon I, Lyon, France; Psychology Department, University of Montreal, QC, Canada. Electronic address:
Machine learning techniques are increasingly used in neuroscience to classify brain signals. Decoding performance is reflected by how much the classification results depart from the rate achieved by purely random classification. In a 2-class or 4-class classification problem, the chance levels are thus 50% or 25% respectively.
View Article and Find Full Text PDFIn silico analysis identifies a C3HC4-RING finger domain of a putative E3 ubiquitin-protein ligase located at the C-terminus of a polyglutamine-containing protein.
Braz J Med Biol Res
March 2007
Departamento de Farmacia, Facultad de Ciencias Químicas, Benemérita Universidad Autónoma de Puebla, 14 Sur Con Avenida San Claudio, Puebla, México.
Almost identical polyglutamine-containing proteins with unknown structures have been found in human, mouse and rat genomes (GenBank AJ277365, AF525300, AY879229). We infer that an identical new gene (RING) finger domain of real interest is located in each C-terminal segment. A three-dimensional (3-D) model was generated by remote homology modeling and the functional implications are discussed.
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