Odor-taste interaction has gained success in enhancing saltiness perception. This work aimed to provide candidate odorants for saltiness enhancement. Volatile compounds and their frequencies in salty foods were systematically analyzed. The compounds ...
The integration of rare disease medical databases belonging to different countries is an important problem, as a large number of observations are required for reliable statistical inference of patient data in order to facilitate clinical research. Su...
Deep learning approaches for multi-label Chest X-ray (CXR) images classification usually require large-scale datasets. However, acquiring such datasets with full annotations is costly, time-consuming, and prone to noisy labels. Therefore, we introduc...
Intrathoracic airway segmentation in computed tomography is a prerequisite for various respiratory disease analyses such as chronic obstructive pulmonary disease, asthma and lung cancer. Due to the low imaging contrast and noises execrated at periphe...
IEEE transactions on neural networks and learning systems
Dec 2, 2024
Accurate lung lesion segmentation from computed tomography (CT) images is crucial to the analysis and diagnosis of lung diseases, such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and the lack of high-quality labeli...
IEEE transactions on neural networks and learning systems
Dec 2, 2024
Transfer learning has attracted considerable attention in medical image analysis because of the limited number of annotated 3-D medical datasets available for training data-driven deep learning models in the real world. We propose Medical Transformer...
The classification codes granted by patent offices are useful instruments for simplifying the bewildering variety of patents in existence. They are singularly unhelpful, however, in locating a specific subgroup of patents such as that of drug-related...
In this study, we revisit named entity recognition (NER) in the biomedical domain from a multimodal perspective, with a particular focus on applications in low-resource languages. Existing research primarily relies on unimodal methods for NER, which ...
In contrast to sentence-level relational extraction, document-level relation extraction poses greater challenges as a document typically contains multiple entities, and one entity may be associated with multiple other entities. Existing methods often...
In this study, we developed a lightweight and rapid convolutional neural network (CNN) architecture for chest X-ray images; it primarily consists of a redesigned feature extraction (FE) module and multiscale feature (MF) module and validated using pu...