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http://dx.doi.org/10.1016/j.hjc.2020.06.012 | DOI Listing |
Microsc Res Tech
January 2025
School of Electrical & Control Engineering, Shenyang Jianzhu University, Shenyang, China.
The atomic force microscope (AFM) image will be inclined and bent due to the tilt angle between the probe and the sample surface. When the least squares fitting method is used to correct the horizontal distortion of the AFM image, the shape structure that is lower or higher than the sample base will affect the final fitting correction result. In view of the limitations of existing methods and the diversity of AFM images, an AFM image level distortion correction method based on automatic feature marking is proposed.
View Article and Find Full Text PDFInt J Surg
January 2025
Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict classical biomarkers, immunotherapy-associated gene signatures, and prognosis-associated subtypes directly from hematoxylin and eosin stained histopathology images. BBMIL showed the best performance among comparative algorithms on the prediction of classical biomarkers, immunotherapy related gene signatures, and subtypes.
View Article and Find Full Text PDFBackground: Transcranial Electrical Stimulation (TES), Temporal Interference Stimulation (TIS), Electroconvulsive Therapy (ECT) and Tumor Treating Fields (TTFields) are based on the application of electric current patterns to the brain.
Objective: The optimal electrode positions, shapes and alignments for generating a desired current pattern in the brain vary between persons due to anatomical variability. The aim is to develop a flexible and efficient computational approach to determine individually optimal montages based on electric field simulations.
EClinicalMedicine
November 2024
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
Background: Attention deficit hyperactivity disorder (ADHD) is one prevalent neurodevelopmental disorder with childhood onset, however, there is no clear correspondence established between clinical ADHD subtypes and primary medications. Identifying objective and reliable neuroimaging markers for categorizing ADHD biotypes may lead to more individualized, biotype-guided treatment.
Methods: Here we proposed a graph convolution network for biological subtype detection (GCN-BSD) using both functional network connectivity (FNC) and non-imaging phenotypic data for ADHD biotype.
Sci Prog
January 2025
Department of Data Science, New Jersey Institute of Technology College of Computing Sciences, Newark, NJ, USA.
Because of their proficiency in capturing the category characteristics of graphs, graph neural networks have shown remarkable advantages for graph-level classification tasks, that is, rumor detection and anomaly detection. Due to the manipulation of special means (e.g.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!