Objective Employing transfer learning (TL) with convolutional neural networks (CNNs), well-
trained on non-medical ImageNet dataset, has shown promising results for medical image …

Despite their success, deep networks are used as black-box models with outputs that are not
easily explainable during the learning and the prediction phases. This lack of interpretability …

Intraoperative diagnosis is essential for providing safe and effective care during cancer
surgery. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin …

To have a better understanding and usage of Convolution Neural Networks (CNNs), the
visualization and interpretation of CNNs has attracted increasing attention in recent years. In …

Deep learning techniques, and in particular Convolutional Neural Networks (CNNs), have
led to significant progress in image processing. Since 2016, many applications for the …

Depression is a common reason for an increase in suicide cases worldwide. Thus, to
mitigate the effects of depression, accurate diagnosis and treatment are needed. An …

Introduction: The radiological reading room is undergoing a paradigm shift to a symbiosis of
computer science and radiology using artificial intelligence integrated with machine and …

Background and objectives Morphological analysis is the starting point for the diagnostic
approach of more than 80% of hematological diseases. However, the morphological …

当前, 人工智能迎来第3 次发展浪潮并在多个领域大数据分析中取得巨大成功,
这为人工智能技术与数值天气预报结合提供了契机. 已有大量研究尝试将人工智能技术用于数值 …

Abstract Starting from Wuhan in China at the end of 2019, coronavirus disease (COVID-19)
has propagated fast all over the world, affecting the lives of billions of people and increasing …