Recently many algorithms for matching three-dimensional medical data have been developed. Inter- and intramodal fusion of data adds valuable information for planning, controlling and evaluating therapies. This work presents a procedure to evaluate the accuracy of fusion algorithms by numerical means. In contrast to the usual way of visual inspection the developed software tools allow automatic numerical--and thus objective--evaluation of different algorithms using simulated realistic volume data. It is therefore possible to conduct reproducible comparisons of different matching methods. These tools also proved to be very valuable during the development and optimisation of an algorithm employing normalised mutual information.
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http://dx.doi.org/10.1515/bmte.2002.47.s1b.626 | DOI Listing |
Front Artif Intell
January 2025
Department of Clinical and Administrative Pharmacy, University of Georgia College of Pharmacy, Augusta, GA, United States.
Background: Large language models (LLMs) have demonstrated impressive performance on medical licensing and diagnosis-related exams. However, comparative evaluations to optimize LLM performance and ability in the domain of comprehensive medication management (CMM) are lacking. The purpose of this evaluation was to test various LLMs performance optimization strategies and performance on critical care pharmacotherapy questions used in the assessment of Doctor of Pharmacy students.
View Article and Find Full Text PDFAm J Neurodegener Dis
December 2024
Department of Radiology, Carver College of Medicine, University of Iowa Iowa, IA 52242, USA.
Objectives: This study aims to explore the capabilities of dendritic learning within feedforward tree networks (FFTN) in comparison to traditional synaptic plasticity models, particularly in the context of digit recognition tasks using the MNIST dataset.
Methods: We employed FFTNs with nonlinear dendritic segment amplification and Hebbian learning rules to enhance computational efficiency. The MNIST dataset, consisting of 70,000 images of handwritten digits, was used for training and testing.
Tzu Chi Med J
July 2024
Department of Radiation Oncology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien, Taiwan.
Objectives: Head-and-neck cancer is a major cancer in Taiwan. Most patients are in the advanced stage at initial diagnosis. In addition to primary surgery, adjuvant therapy, including chemotherapy and radiotherapy, is also necessary to treat these patients.
View Article and Find Full Text PDFFront Plant Sci
January 2025
Research Institute of Forest Policy and Information, Chinese Academy of Forestry, Beijing, China.
The processing of LiDAR point cloud data is of critical importance in the context of forest resource surveys, as well as representing a pivotal element in the realm of forest physiological and ecological studies.Nonetheless, conventional denoising algorithms frequently exhibit deficiencies with regard to adaptability and denoising efficacy, particularly when employed in relation to disparate datasets.To address these issues, this study introduces DEN4, an unsupervised, deep learning-based point cloud denoising algorithm designed to improve the accuracy of single tree segmentation in LiDAR point clouds.
View Article and Find Full Text PDFGates Open Res
January 2025
University of Virginia, Charlottesville, Virginia, USA.
Background: The TaqMan Array Card (TAC) is an arrayed, high-throughput qPCR platform that can simultaneously detect multiple targets in a single reaction. However, the manual post-run analysis of TAC data is time consuming and subject to interpretation. We sought to automate the post-run analysis of TAC data using machine learning models.
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