Machine learning models often rely on simple spurious features - patterns in training data that correlate with targets but are not causally related to them, like image backgrounds in foreground classification. This reliance typically leads to imbalan...
The most common types of kidneys and liver cancer are renal cell carcinoma (RCC) and hepatic cell carcinoma (HCC), respectively. Accurate grading of these carcinomas is essential for determining the most appropriate treatment strategies, including su...
Artificial neural networks (ANNs) have been shown to predict neural responses in primary visual cortex (V1) better than classical models. However, this performance often comes at the expense of simplicity and interpretability. Here we introduce a new...
Ankylosing Spondylitis (AS), commonly known as Bechterew's disease, is a complex, potentially disabling disease that develops slowly over time and progresses to radiographic sacroiliitis. The etiology of this disease is poorly understood, making it d...
This paper evaluates the use of impedance spectroscopy combined with artificial intelligence. Both technologies have been widely used in food classification and it is proposed a way to improve classifications using recurrent neural networks that trea...
Against the backdrop of rapid advancements in artificial intelligence (AI), multimodal deep learning (DL) technologies offer new possibilities for cross-language translation. This work proposes a multimodal DL-based translation model, the Transformer...
Blood pressure (BP) serves as a fundamental indicator of cardiovascular health, measuring the force exerted by circulating blood against arterial walls during each heartbeat. This paper introduces an advanced deep learning framework for precise, non-...
Anterior Cruciate Ligament (ACL) tears are common in sports and can provide noteworthy health issues. Therefore, accurately diagnosing of tears is important for the early and proper treatment. However, traditional diagnostic methods, such as clinical...
This study aimed to develop and validate convolutional neural network (CNN) models for distinguishing follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA). Additionally, this current study compared the performance of CNN models wi...
Graph neural networks (GNNs) have shown great promise for representation learning on complex graph-structured data, but existing models often fall short when applied to directed heterogeneous graphs. In this study, we proposed a novel embedding metho...
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