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Class-Wise Combination of Mixture-Based Data Augmentation for Class Imbalance Learning of Focal Liver Lesions in Abdominal CT Images.
J Imaging Inform Med
January 2025
Department of Software Convergence, Seoul Women's University, Hwarango 621, Nowongu, Seoul, 01797, Republic of Korea.
In this paper, we propose a method to address the class imbalance learning in the classification of focal liver lesions (FLLs) from abdominal CT images. Class imbalance is a significant challenge in medical image analysis, making it difficult for machine learning models to learn to classify them accurately. To overcome this, we propose a class-wise combination of mixture-based data augmentation (CCDA) method that uses two mixture-based data augmentation techniques, MixUp and AugMix.
View Article and Find Full Text PDFMulti-level Factors to Build Confidence and Support in Active Surveillance for Low-Risk Prostate Cancer: A Qualitative Study.
J Gen Intern Med
January 2025
Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Background: Active surveillance (AS) is the guideline-recommended treatment for low-risk prostate cancer and involves routine provider visits, lab tests, imaging, and prostate biopsies. Despite good uptake, adherence to AS, in terms of receiving recommended follow-up testing and remaining on AS in the absence of evidence of cancer progression, remains challenging.
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Recursive hierarchical parametric identification of Wiener-Hammerstein systems based on initial value optimization.
ISA Trans
January 2025
Institute of Artificial Intelligence and Future Networks, Beijing Normal University at Zhuhai, Zhuhai, China; BNU-HKBU United International College Tangjiawan, Rd. JinTong 2000#, Zhuhai, China. Electronic address:
In this paper, a novel recursive hierarchical parametric identification method based on initial value optimization is proposed for Wiener-Hammerstein systems subject to stochastic measurement noise. By transforming the traditional Wiener-Hammerstein system model into a generalized form, the system model parameters are uniquely expressed for estimation. To avoid cross-coupling between estimating block-oriented model parameters, a hierarchical identification algorithm is presented by dividing the parameter vector into two subvectors containing the coupled and uncoupled terms for estimation, respectively.
View Article and Find Full Text PDFEndoVIA for quantifying A-to-I editing and mapping the subcellular localization of edited transcripts.
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Department of Chemistry, Washington University in St. Louis, MO, United States. Electronic address:
Adenosine-to-inosine (A-to-I) editing, catalyzed by adenosine deaminases acting on RNA (ADARs), is a prevalent post-transcriptional modification that is vital for numerous biological functions. Given that this modification impacts global gene expression, RNA localization, and innate cellular immunity, dysregulation of A-to-I editing has unsurprisingly been linked to a variety of cancers and other diseases. However, our current understanding of the underpinning mechanisms that connect dysregulated A-to-I editing and disease processes remains limited.
View Article and Find Full Text PDFBrain State Forecasting for Precise Brain Stimulation: Current Approaches and Future Perspectives.
Neuroimage
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Center for Mind/Brain Sciences (CIMeC), University of Trento, 38068, Rovereto, (TN), Italy.
Transcranial magnetic stimulation (TMS) has the potential to yield insights into cortical functions and improve the treatment of neurological and psychiatric conditions. However, its reliability is hindered by a low reproducibility of results. Among other factors, such low reproducibility is due to structural and functional variability between individual brains.
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