Deep neural networks are known to be data-driven and label noise can have a marked impact on model performance. Recent studies have shown great robustness to classic image recognition even under a high noisy rate. In medical applications, learning fr...
In the last years, deep learning has dramatically improved the performances in a variety of medical image analysis applications. Among different types of deep learning models, convolutional neural networks have been among the most successful and they...
The demand for cost-efficient manufacturing of complex metal components has driven research for metal Additive Manufacturing (AM) such as Wire + Arc Additive Manufacturing (WAAM). WAAM enables automated, time- and material-efficient manufacturing of ...
Improvementsin medical imaging and a steady increase in computing power are leading to new possibilities in the field of cardiovascular interventions. Interventions can be planned in advance in greater detail, even to the point of simulating procedur...
Computational intelligence and neuroscience
May 18, 2022
Smart medical uses the medical information platform and the current technological means to enable the process of sharing information between medical staff and medical equipment. The combination of current technology and the medical field has become t...
The COVID-19 pandemic has changed the way we practice medicine. Cancer patient and obstetric care landscapes have been distorted. Delaying cancer diagnosis or maternal-fetal monitoring increased the number of preventable deaths or pregnancy complicat...
Acute pulmonary embolism (PE) is a critical, potentially life-threatening finding on contrast-enhanced cross-sectional chest imaging. Timely and accurate diagnosis of thrombus acuity and extent directly influences patient management, and outcomes. Te...
The use of technology in medicine has grown exponentially because of the technological advancements allowing the digitization of medical data and optimization of their processing to extract multiple features of significant clinical relevance. Radiolo...
Deep learning has been extensively applied to segmentation in medical imaging. U-Net proposed in 2015 shows the advantages of accurate segmentation of small targets and its scalable network architecture. With the increasing requirements for the perfo...
IEEE journal of biomedical and health informatics
Apr 14, 2022
A key challenge in training neural networks for a given medical imaging task is the difficulty of obtaining a sufficient number of manually labeled examples. In contrast, textual imaging reports are often readily available in medical records and cont...
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