Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …

Pretrained neural network models for biological segmentation can provide good out-of-the-
box results for many image types. However, such models do not allow users to adapt the …

A generative adversarial network (GAN) is one of the most significant research directions in
the field of artificial intelligence, and its superior data generation capability has garnered …

Few-shot learning in image classification is developed to learn a model that aims to identify
unseen classes with only few training samples for each class. Fewer training samples and …

Internet of Medical Things (IoMT) can connect many medical imaging equipment to the
medical information network to facilitate the process of diagnosing and treating doctors. As …

Deep neural networks represent, nowadays, the most effective machine learning technology
in biomedical domain. In this domain, the different areas of interest concern the Omics (study …

Gliomas are the most common primary brain malignancies, with different degrees of
aggressiveness, variable prognosis and various heterogeneous histological sub-regions, ie …

The appearance of generative adversarial networks (GAN) provides a new approach and
framework for computer vision. Compared with traditional machine learning algorithms, GAN …

Breast mass is one of the most distinctive signs for the diagnosis of breast cancer, and the
accurate segmentation of masses is critical for improving the accuracy of breast cancer …

Recent advancements with deep generative models have proven significant potential in the
task of image synthesis, detection, segmentation, and classification. Segmenting the medical …