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A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.
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http://dx.doi.org/10.1109/tsmcb.2003.818440 | DOI Listing |
Med Phys
March 2025
School of Computer Science and Engineering, Central South University, Changsha, China.
Background: Quantitative susceptibility mapping (QSM) is a post-processing magnetic resonance imaging (MRI) technique that extracts the distribution of tissue susceptibilities and holds significant promise in the study of neurological diseases. However, the ill-conditioned nature of dipole inversion often results in noise and artifacts during QSM reconstruction from the tissue field. Deep learning methods have shown great potential in addressing these issues; however, most existing approaches rely on basic U-net structures, leading to limited performances and reconstruction artifacts sometimes.
View Article and Find Full Text PDFMed Phys
March 2025
Center of Statistical Research and School of Statistics, Southwestern University of Finance and Economics, Chengdu, China.
Background: Recently, deep convolutional neural networks (CNNs) have shown great potential in medical image classification tasks. However, the practical usage of the methods is constrained by two challenges: 1) the challenge of using nonindependent and identically distributed (non-IID) datasets from various medical institutions while ensuring privacy, and 2) the data imbalance problem due to the frequency of different diseases.
Purpose: The objective of this paper is to present a novel approach for addressing these challenges through a decentralized learning method using a prototypical contrastive network to achieve precise medical image classification while mitigating the non-IID problem across different clients.
Hepatol Int
March 2025
Department of Health Technology and Informatics, Hong Kong Polytechnic University, 11 Yuk Choi Road, Hong Kong SAR, China.
Purpose: Existing prognostic staging systems depend on expensive manual extraction by pathologists, potentially overlooking latent patterns critical for prognosis, or use black-box deep learning models, limiting clinical acceptance. This study introduces a novel deep learning-assisted paradigm that complements existing approaches by generating interpretable, multi-view risk scores to stratify prognostic risk in hepatocellular carcinoma (HCC) patients.
Methods: 510 HCC patients were enrolled in an internal dataset (SYSUCC) as training and validation cohorts to develop the Hybrid Deep Score (HDS).
Ann Nucl Med
March 2025
Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Objective: To provide fully automatic scanner-independent 5-level categorization of the [I]FP-CIT uptake in striatal subregions in dopamine transporter SPECT.
Methods: A total of 3500 [I]FP-CIT SPECT scans from two in house (n = 1740, n = 640) and two external (n = 645, n = 475) datasets were used for this study. A convolutional neural network (CNN) was trained for the categorization of the [I]FP-CIT uptake in unilateral caudate and putamen in both hemispheres according to 5 levels: normal, borderline, moderate reduction, strong reduction, almost missing.
Adv Sci (Weinh)
March 2025
Department of Neurobiology, Hebei Medical University, Shijiazhuang, 050017, China.
The dynamic interaction between central respiratory chemoreceptors and the respiratory central pattern generator constitutes a critical homeostatic axis for stabilizing breathing rhythm and pattern, yet its circuit-level organization remains poorly characterized. Here, the functional connectivity between two key medullary hubs: the nucleus tractus solitarius (NTS) and the preBötzinger complex (preBötC) are systematically investigated. These findings delineate a medullary network primarily comprising Phox2b-expressing NTS neurons (NTS), GABAergic NTS neurons (NTS), and somatostatin (SST)-expressing preBötC neurons (preBötC).
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