To design fast neural networks, many works have been focusing on reducing the number of
floating-point operations (FLOPs). We observe that such reduction in FLOPs, however, does …

Abstract Convolutional Neural Networks (CNNs) have achieved remarkable performance in
various computer vision tasks but this comes at the cost of tremendous computational …

Recently, some pioneering works have preferred applying more complex modules to
improve segmentation performances. However, it is not friendly for actual clinical …

Fundus images are an essential basis for diagnosing ocular diseases, and using
convolutional neural networks has shown promising results in achieving accurate fundus …

Recently, Visual Transformer (ViT) has been widely used in various fields of computer vision
due to applying self-attention mechanism in the spatial domain to modeling global …

Abstract Recently, Visual Transformer (ViT) has been extensively used in medical image
segmentation (MIS) due to applying self-attention mechanism in the spatial domain to …

Identifying early a person with dyslexia, a learning disorder with reading and writing, is
critical for effective treatment. As accredited specialists for clinical diagnosis of dyslexia are …

Accurately identifying and localising small objects within images or videos is a critical
challenge in the field of computer vision. It is mostly applied in scenarios that require high …

In recent years, convolutional neural networks (CNNs) have played a dominant role in facial
expression recognition. While CNN-based methods have achieved remarkable success …

Medical image segmentation models often fail to generalize well when applied to new
datasets, hindering their usage in clinical practice. Existing random-convolution-based …