New developments in electron microscopy technology, improved efficiency of detectors, and artificial intelligence applications for data analysis over the past decade have increased the use of volume electron microscopy (vEM) in the life sciences fiel...
Current models on Explainable Artificial Intelligence (XAI) have shown a lack of reliability when evaluating feature-relevance for deep neural biomarker classifiers. The inclusion of reliable saliency-maps for obtaining trustworthy and interpretable ...
An increasing number of patients and a lack of awareness about obstructive sleep apnea is a point of concern for the healthcare industry. Polysomnography is recommended by health experts to detect obstructive sleep apnea. The patient is paired up wit...
One of the most severe types of cancer caused by the uncontrollable proliferation of brain cells inside the skull is brain tumors. Hence, a fast and accurate tumor detection method is critical for the patient's health. Many automated artificial intel...
Manual segmentation, which is tedious, time-consuming, and operator-dependent, is currently used as the gold standard to validate automatic and semiautomatic methods that quantify geometries from 2D and 3D MR images. This study examines the accuracy ...
BMC medical informatics and decision making
May 10, 2023
BACKGROUND: We propose a new deep learning model to identify unnecessary hemoglobin (Hgb) tests for patients admitted to the hospital, which can help reduce health risks and healthcare costs.
Journal of molecular graphics & modelling
May 9, 2023
Molecular properties prediction and new material discovery are significant for the pharmaceutical industry, food, chemistry, and other fields. The popular methods are theoretical mechanism calculation and machine learning. There is a deviation betwee...
Micro-computed tomography (µCT)-based imaging plays a key role in monitoring disease progression and response to candidate drugs in various animal models of human disease, but manual image processing is still highly time-consuming and prone to operat...
Uncertainty estimation is crucial for understanding the reliability of deep learning (DL) predictions, and critical for deploying DL in the clinic. Differences between training and production datasets can lead to incorrect predictions with underestim...
Chemical exchange saturation transfer (CEST) MRI is a promising molecular imaging technique but suffers from long scan times and complicated processing. CEST was recently combined with magnetic resonance fingerprinting (MRF) to address these shortcom...
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