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Fast motion-compensated reconstruction for 4D-CBCT using deep learning-based groupwise registration.
Biomed Phys Eng Express
December 2024
Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, MO, United States of America.
. Previous work has that deep learning (DL)-enhanced 4D cone beam computed tomography (4D-CBCT) images improve motion modeling and subsequent motion-compensated (MoCo) reconstruction for 4D-CBCT. However, building the motion model at treatment time via conventional deformable image registration (DIR) methods is not temporally feasible.
View Article and Find Full Text PDFEvolutionary neural architecture search for automated MDD diagnosis using multimodal MRI imaging.
iScience
October 2024
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
Major depressive disorder (MDD) is a prevalent mental disorder with serious impacts on life and health. Neuroimaging offers valuable diagnostic insights. However, traditional computer-aided diagnosis methods are limited by reliance on researchers' experience.
View Article and Find Full Text PDFDifferences in Mu rhythm when seeing grasping/motor actions in a real context versus on screens.
Sci Rep
October 2024
Division of Neurosciences, University Pablo de Olavide, Seville, 41013, Spain.
Mu rhythm (∼8-12 Hz) in the somatosensory cortex has traditionally been linked with doing and seeing motor activities. Here, we aimed to learn how the medium (physical or screened) in which motor actions are seen could impact on that specific brain rhythm. To do so, we presented to 40 participants the very same narrative content both in a one-shot movie with no cuts and in a real theatrical performance.
View Article and Find Full Text PDFOS-SSVEP: One-shot SSVEP classification.
Neural Netw
December 2024
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, China; Center for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE), Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, China; Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui, 230026, China; Key Laboratory of Intelligent Information Processing of the Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China. Electronic address:
It is extremely challenging to classify steady-state visual evoked potentials (SSVEPs) in scenarios characterized by a huge scarcity of calibration data where only one calibration trial is available for each stimulus target. To address this challenge, we introduce a novel approach named OS-SSVEP, which combines a dual domain cross-subject fusion network (CSDuDoFN) with the task-related and task-discriminant component analysis (TRCA and TDCA) based on data augmentation. The CSDuDoFN framework is designed to comprehensively transfer information from source subjects, while TRCA and TDCA are employed to exploit the information from the single available calibration trial of the target subject.
View Article and Find Full Text PDFOLR-Net: Object Label Retrieval Network for principal diagnosis extraction.
Comput Biol Med
November 2024
Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou 570228, China; College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou 310027, China.
Background: Extracting principal diagnosis from patient discharge summaries is an essential task for the meaningful use of medical data. The extraction process, usually by medical staff, is laborious and time-consuming. Although automatic models have been proposed to retrieve principal diagnoses from medical records, many rare diagnoses and a small amount of training data per rare diagnosis provide significant statistical and computational challenges.
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