The accurate calibration of powder diffraction data acquired from area detectors using calibration standards is a crucial step in the data reduction process to attain high-quality one-dimensional patterns. A novel algorithm has been developed for extracting Debye-Scherrer rings automatically using an approach based on computer vision and pattern recognition techniques. The presented technique requires no human intervention and, unlike previous approaches, makes no restrictive assumptions on the diffraction setup and/or rings. It can detect complete rings as well as portions of them, and works on several types of diffraction images with various degrees of ring graininess, textured diffraction patterns and detector tilt with respect to the incoming beam.
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http://dx.doi.org/10.1107/S1600577518000425 | DOI Listing |
PLoS Comput Biol
January 2025
Department of Computer Science, Colorado State University, Fort Collins, Colorado, United States of America.
Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and computer vision. By providing pre-trained models that can be fine-tuned for specific applications, they enable researchers to create accurate models with minimal effort and computational resources. Large scale genomics deep learning models come in two flavors: the first are large language models of DNA sequences trained in a self-supervised fashion, similar to the corresponding natural language models; the second are supervised learning models that leverage large scale genomics datasets from ENCODE and other sources.
View Article and Find Full Text PDFOtolaryngol Head Neck Surg
January 2025
Department of Otolaryngology-Head and Neck Surgery, University of Washington, Seattle, Washington, USA.
Objective: To validate the use of neural radiance fields (NeRF), a state-of-the-art computer vision technique, for rapid, high-fidelity 3-dimensional (3D) reconstruction in endoscopic sinus surgery (ESS).
Study Design: An experimental cadaveric pilot study.
Setting: Academic medical center.
Cardiovasc Diagn Ther
December 2024
East Slovak Institute of Cardiovascular Diseases and School of Medicine, Pavol Jozef Safarik University, Kosice, Slovakia.
Background: Echocardiography is widely used to assess aortic stenosis (AS) but can yield inconsistent results, leading to uncertainty about AS severity and the need for further diagnostics. This retrospective study aimed to evaluate a novel echocardiography-based marker, the signal intensity coefficient (SIC), for its potential in accurately identifying and quantifying calcium in AS, enhancing noninvasive diagnostic methods.
Methods: Between May 2022 and October 2023, 112 cases of AS that were previously considered severe by echocardiography were retrospectively evaluated, as well as a group of 50 cases of mild or moderate AS, both at the Eastern Slovak Institute of Cardiovascular Diseases in Kosice, Slovakia.
Int J Comput Assist Radiol Surg
January 2025
Computer Vision and Image Processing Lab., UofL, Louisville, KY, 40292, USA.
Purpose: This article introduces a novel deep learning approach to substantially improve the accuracy of colon segmentation even with limited data annotation, which enhances the overall effectiveness of the CT colonography pipeline in clinical settings.
Methods: The proposed approach integrates 3D contextual information via guided sequential episodic training in which a query CT slice is segmented by exploiting its previous labeled CT slice (i.e.
J Pediatr
January 2025
Nanit Research Department, New York, New York.
Objective: To examine prospectively the relationship between teething and infant sleep using objective sleep measurements.
Study Design: Over a 4-week period, 849 infants aged 3-18 months (mean = 8.4 ± 1.
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