Latest AI and machine learning research in work force for healthcare professionals.
To mitigate the shortage of labeled data, Few-Shot Classification (FSC) methods train deep neural networks (DNNs) on a base dataset with sufficient labeled data, and then adapt them to target tasks using a few labeled data. Despite notable progress, a single FSC model remains prone to high variance and low confidence. As a result, ensemble FSC has garnered increasing attention. However, the limite...
OBJECTIVE: Telehealth paradigms are essential for remotely managing patients with chronic conditions. To assist clinicians in handling the large volumes of data collected through these systems, clinical decision support systems (CDSSs) have been developed. However, the effectiveness of CDSSs depends on the quality of remotely recorded physiological data and the reliability of the algorithms used f...
This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of post-traumatic stress disorder (PTSD) predictive models. We...
PURPOSE OF REVIEW: This review explores the variability in preimplantation kidney biopsy processing methods, emphasizing their impact on histological ...
BACKGROUND: Patient engagement is a critical but challenging public health priority in behavioral health care. During telehealth sessions, health care...
Generative Artificial Intelligence (Gen AI) has transformative potential in healthcare to enhance patient care, personalize treatment options, train h...
Tea is an important economic product in China, and tea picking is a key agricultural activity. As the practice of tea picking in China gradually shift...
BACKGROUND: The escalating global scarcity of skilled health care professionals is a critical concern, further exacerbated by rising stress levels and...
Amid rapid urbanization, land use shifts in cities globally have profound effects on ecosystems and biodiversity. Birds, as a crucial component of urb...
Dredging in estuarine systems significantly impacts phytoplankton communities, with suspended particulate matter (SPM) and dissolved aluminum (Al) ser...
Active learning on graphs (ALG) has emerged as a compelling research field due to its capacity to address the challenge of label scarcity. Existing AL...
BACKGROUND: Chronic kidney disease (CKD) imposes a significant global health and economic burden, impacting millions globally. Despite its high preval...
Out-of-graph node representation learning aims at learning about newly arrived nodes for a dynamic graph. It has wide applications ranging from commun...
Postpartum depression (PPD) poses significant risks to maternal and infant health, yet proteomic analyses of PPD-risk women remain limited. This study...
Adversarial training has become a primary method for enhancing the robustness of deep learning models. In recent years, fast adversarial training meth...
Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression l...
OBJECTIVE: Rapid on-site evaluation (ROSE) of respiratory cytology specimens is a critical technique for accurate and timely diagnosis of lung cancer....
Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as a surrogate marker fo...
The recent advent of in-context learning (ICL) capabilities in large pre-trained models has yielded significant advancements in the generalization of ...
Ovarian lesions are common and often incidentally detected. A critical shortage of expert ultrasound examiners has raised concerns of unnecessary inte...