Stroke is a medical condition that affects around 15 million people annually. Patients and
their families can face severe financial and emotional challenges as it can cause motor …

Accidental falls pose a significant public health challenge, especially among vulnerable
populations. To address this issue, comprehensive research on fall detection and rescue …

Simple Summary Transfer learning is a technique utilizing a pre-trained model's knowledge
in a new task. This helps reduce the sample size and time needed for training. These …

Objective This study aimed to develop and validate a predictive model for osteoporotic
vertebral fractures (OVFs) risk by integrating demographic, bone mineral density (BMD), CT …

Low visibility always leads to serious traffic accidents worldwide, although extensive works
are studied to deal with the estimation of visibility in meteorology areas, it is still a tough …

Deep learning has shown significant value in automating radiological diagnostics but can be
limited by a lack of generalizability to external datasets. Leveraging the geometric principles …

Transfer learning (TL) is an alternative approach to the full training of deep learning (DL)
models from scratch and can transfer knowledge gained from large-scale data to solve …

Rationale and Objectives To construct and validate a deep learning radiomics (DLR) model
based on X-ray images for predicting and distinguishing acute and chronic osteoporotic …

Domain adaptation (DA) is a statistical learning problem that arises when the distribution of
the source data used to train a model differs from that of the target data used to evaluate the …

We compare the effectiveness of using a trained-from-scratch, unsupervised deep
generative Variational Autoencoder (VAE) model as a solution to generic representation …