Biomedical physics & engineering express
Nov 6, 2025
. Most low-dose computed tomography (LDCT) denoising methods based on CNN have some denoising effect, but their interpretability is very low due to the black-box nature of neural networks.. To address this issue, we propose a novel fully sparse-regul...
Journal of radiological protection : official journal of the Society for Radiological Protection
Nov 6, 2025
Dose estimation in response to internal radionuclide exposures requires reconstruction of the initial intake activity, which is frequently unknown due to the absence ofdata. In such scenarios, intake is inferred from bioassay measurements obtained at...
BMC medical informatics and decision making
Nov 5, 2025
Cervical cancer is the leading cause of cancer-related deaths among women worldwide, necessitating early and accurate detection methods. This study introduces a hybrid framework utilizing Vision Transformers (ViT) and ensemble learning-based convolut...
Recent advances in spatial transcriptomics (ST) highlight the need to integrate multiple slices for joint analysis. A key challenge is generating interpretable embeddings that preserve spatial geometry while correcting batch effects. We present MaskG...
BACKGROUND: This study introduces a novel deep learning methodology for the automated detection of a wide range of dental prostheses, including crowns, bridges, and implants, as well as various dental treatments such as fillings, root canal therapies...
The endoplasmic reticulum (ER) comprises smooth tubules, ribosome-studded sheets, and peripheral sheets that can present as tubular matrices. ER shaping proteins determine ER morphology, however, understanding their role in tubular matrix formation r...
The aim of this study was to develop a convolutional neural network (CNN)-based end-to-end learning architecture to predict the root development stages of permanent first molar teeth using panoramic radiographs. A dataset of 1629 first molar images w...
Spiking Neural Networks (SNNs), designed to more accurately model the brain's neurobiological processes, have been proposed as energy-efficient alternatives to conventional Artificial Neural Networks (ANNs), which typically incur high computational a...
Computer vision, a rapidly advancing branch of artificial intelligence (AI), has gained significant attention in medical and dental applications. Semantic segmentation, a key technique within computer vision, enables the precise identification and de...
Driver drowsiness is a leading cause of traffic accidents and fatalities, highlighting the urgent need for intelligent systems capable of real-time fatigue detection. Although recent advancements in machine learning (ML) and deep learning (DL) have s...
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