In this work, a novel data validation algorithm for a single-camera SLAM system is introduced. A 6-degree-of-freedom monocular SLAM method based on the delayed inverse-depth (DI-D) feature initialization is used as a benchmark. This SLAM methodology has been improved with the introduction of the proposed data association batch validation technique, the highest order hypothesis compatibility test, HOHCT. This new algorithm is based on the evaluation of statistically compatible hypotheses, and a search algorithm designed to exploit the characteristics of delayed inverse-depth technique. In order to show the capabilities of the proposed technique, experimental tests have been compared with classical methods. The results of the proposed technique outperformed the results of the classical approaches.
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http://dx.doi.org/10.1016/j.isatra.2013.04.008 | DOI Listing |
Biol Direct
January 2025
School of Medicine, South China University of Technology, Guangzhou, 510006, China.
Background: Pancreatic cancer is characterized by a complex tumor microenvironment that hinders effective immunotherapy. Identifying key factors that regulate the immunosuppressive landscape is crucial for improving treatment strategies.
Methods: We constructed a prognostic and risk assessment model for pancreatic cancer using 101 machine learning algorithms, identifying OSBPL3 as a key gene associated with disease progression and prognosis.
J Orthop Surg Res
January 2025
Department of Hand-Foot Microsurgery, Shenzhen Nanshan People's Hospital, The 6th Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China.
Background: Steroid-induced osteonecrosis of the femoral head (SIONFH) is a universal hip articular disease and is very hard to perceive at an early stage. The understanding of the pathogenesis of SIONFH is still limited, and the identification of efficient diagnostic biomarkers is insufficient. This research aims to recognize and validate the latent exosome-related molecular signature in SIONFH diagnosis by employing bioinformatics to investigate exosome-related mechanisms in SIONFH.
View Article and Find Full Text PDFBMC Cancer
January 2025
Department of Radiology, Henan Provincial People's Hospital & Zhengzhou University People's Hospital, Zhengzhou, Henan, China.
Objectives: To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical variables for evaluating TP53 mutations in endometrial cancer (EC).
Methods: DWI and clinical data from 155 EC patients were included in this study, consisting of 80 in the training set, 35 in the test set, and 40 in the external validation set. Radiomics features, convolutional neural network-based DL features, and clinical variables were analyzed.
BMC Psychiatry
January 2025
Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Background: Mental disorders are increasingly prevalent, leading to increased medical expenditures. To refine the reimbursement of medical costs for inpatients with mental disorders by health insurance, an accurate prediction model is essential. Per-diem payment is a common internationally implemented payment method for medical insurance of inpatients with mental disorders, necessitating the exploration of advanced machine learning methods for predicting the average daily hospitalization costs (ADHC) based on the characteristics of inpatients with mental disorders.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Radiology, UC Davis School of Medicine, University of California, Davis, 4860 Y Street, Suite 3100, Sacramento, CA, 95817-2307, USA.
Purpose: To explore the information in routine digital subtraction angiography (DSA) and evaluate deep learning algorithms for automated identification of anatomic location in DSA sequences.
Methods: DSA of the abdominal aorta, celiac, superior mesenteric, inferior mesenteric, and bilateral external iliac arteries was labeled with the anatomic location from retrospectively collected endovascular procedures performed between 2010 and 2020 at a tertiary care medical center. "Key" images within each sequence demonstrating the parent vessel and the first bifurcation were additionally labeled.
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