Cell detection and segmentation are integral parts of automated systems in digital pathology. Encoder-decoder networks have emerged as a promising solution for these tasks. However, training of these networks has typically required full boundary anno...
BACKGROUND: The escalating mental health crisis during and post-COVID-19 underscores the urgent need for scalable, timely, cost-effective assessment solutions for general psychotic disorders. Regretfully, traditional symptom-based, one-to-one assessm...
Reliability in diagnosing and treating brain tumors depends on the accurate grading of histopathological images. However, limited scalability, adaptability, and interpretability challenge current methods for frequently grading brain tumors to accurat...
Accurate differential diagnosis of pneumonia remains a challenging task, as different types of pneumonia require distinct treatment strategies. Early and precise diagnosis is crucial for minimizing the risk of misdiagnosis and for effectively guiding...
Globally, nervous system diseases are the leading cause of disability-adjusted life-years and the second leading cause of mortality in the world. Traditional diagnostic methods for nervous system diseases are expensive. So this study aimed to constru...
Old and vision-impaired indoor action monitoring utilizes sensor technology to observe movement and interaction in the living area. This model can recognize changes from regular patterns, deliver alerts, and ensure safety in case of any dangers or la...
The Neuron morphology classification is a critical task in neuroscience research, as the morphological features of neurons are closely linked to the functional characteristics of neural circuits. However, traditional classification methods often stru...
Medical image analysis using deep learning algorithms has become a basis of modern healthcare, enabling early detection, diagnosis, treatment planning, and disease monitoring. However, sharing sensitive raw medical data with third parties for analysi...
Human prefrontal areas show enhanced activations when individuals are presented with images, under diverse task conditions. However, the functional role of these increased activations remains a deeply debated question. Here we addressed this question...
Physics-informed neural networks (PINNs) represent a transformative approach to data models by incorporating known physical laws into neural network training, thereby improving model generalizability, reduce data dependency, and enhance interpretabil...
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