This article reviews deep learning methods for medical imaging (focusing on image
reconstruction, segmentation, registration, and radiomics) and radiotherapy (ranging from …

In recent years, driven by the continuous development of mobile Internet technology and
artificial intelligence technology, the improvement of the manufacturing level of 6G Internet of …

Dropout as a regularizer in deep neural networks has been less effective in convolutional
layers than in fully connected layers. This is due to the fact that dropout drops features …

The increasing of public neuroimaging datasets opens a door to analyzing homogeneous
human brain conditions across datasets by transfer learning (TL). However, neuroimaging …

Understanding the human brain is a long-standing challenge in science. In this thesis, we
focus on the brain decoding problem, where we estimate a cognitive state from functional …

We are constantly surrounded by sounds that we continuously exploit to adapt our actions to
situations we are facing. Some of the sounds like speech can have a particular structure …

In recent years, the number of functional Magnetic Resonance Imaging (fMRI) datasets
sharply increased. As a result, a larger set of methods in artificial intelligence domain, that …

Despite the success of deep learning in domains such as image, voice, and graphs, there
has been little progress in deep representation learning for domains without a known …