Over the years, toxicity prediction has been a challenging task. Artificial intelligence and machine learning provide a platform to study toxicity prediction more accurately with a reduced time span. An optimized ensembled model is used to contrast t...
The task of generating medical reports automatically is of paramount importance in modern healthcare, offering a substantial reduction in the workload of radiologists and accelerating the processes of clinical diagnosis and treatment. Current challen...
Deep learning models are increasingly being implemented for automated medical image analysis to inform patient care. Most models, however, lack uncertainty information, without which the reliability of model outputs cannot be ensured. Several uncerta...
Mimicking the olfactory system of humans, the use of electronic noses (E-noses) for the detection of odors in nature has become a hot research topic. This study presents a novel E-nose based on deep learning architecture called Scentformer, which add...
As metabolic byproducts and representative indicators of food spoilage, the monitoring and detection for biogenic amines (BAs) are crucial but challenging for food quality assessment. Here, a strategy is proposed by combining fluorescent probe molecu...
This study is aimed to clarify the effectiveness of the image-rotation technique and zooming augmentation to improve the accuracy of the deep learning (DL)-based dose conversion from pencil beam (PB) to Monte Carlo (MC) in proton beam therapy (PBT). ...
OBJECTIVE: This study aims to develop ultrasound biomicroscopy (UBM)-based artificial intelligence (AI) models for preoperative differentiation of acute angle closure (AAC) with or without zonulopathy and to compare their comprehensive diagnostic per...
BACKGROUND: Neurological disorders, ranging from common conditions like Alzheimer's disease that is a progressive neurodegenerative disorder and remains the most common cause of dementia worldwide to rare disorders such as Angelman syndrome, impose a...
Sepsis remains a leading cause of mortality in critical care settings, necessitating timely and accurate risk stratification. However, existing machine learning models for sepsis prediction often suffer from poor interpretability, limited generalizab...
This article introduces a novel deep-learning based framework, Super-resolution/Denoising network (SDNet), for simultaneous denoising and super-resolution of swept-source optical coherence tomography (SS-OCT) images. The novelty of this work lies in ...
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