The Journal of the Acoustical Society of America
Jan 1, 2025
Odontocetes are capable of dynamically changing their echolocation clicks to efficiently detect targets, and learning their clicking strategy can facilitate the design of man-made detecting signals. In this study, we developed deep convolutional gene...
This study presents a novel integration of two advanced deep learning models, U-Net and EfficientNetV2, to achieve high-precision segmentation and rapid classification of pathological images. A key innovation is the development of a new heatmap gener...
BACKGROUND: Medical images play an important role in diagnosis and treatment of pediatric solid tumors. The field of radiology, pathology, and other image-based diagnostics are getting increasingly important and advanced. This indicates a need for ad...
Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been raised regarding potential contrast agent accumulation and toxicity. Purpose To evaluate the feasib...
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2025
SNARE proteins play a pivotal role in membrane fusion and various cellular processes. Accurate identification of SNARE proteins is crucial for elucidating their functions in both health and disease contexts. This chapter presents a novel approach emp...
BACKGROUND: Accurate diagnosis and classification of Alzheimer's disease (AD) are crucial for effective treatment and management. Traditional diagnostic models, largely based on binary classification systems, fail to adequately capture the complexiti...
Explore deep learning applications in predictive analytics for public health data, identify challenges and trends, and then understand the current landscape. A systematic literature review was conducted in June 2023 to search articles on public hea...
Purpose To validate a deep learning (DL) model for predicting the risk of prostate cancer (PCa) progression based on MRI and clinical parameters and compare it with established models. Materials and Methods This retrospective study included 1607 MRI ...
OBJECTIVE: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI.
OBJECTIVE: The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2) and a conventional T...
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