Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor
problems for people with a detrimental effect on the functioning of the nervous system. In …

The advent of deep learning has brought great change to the community of computer
science and also revitalized numerous fields where traditional machine learning methods …

Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …

We present a critical assessment of the role of transfer learning in training fully convolutional
networks (FCNs) for medical image segmentation. We first show that although transfer …

(1) Background: Transfer learning refers to machine learning techniques that focus on
acquiring knowledge from related tasks to improve generalization in the tasks of interest. In …

Background The increased availability and usage of modern medical imaging induced a
strong need for automatic medical image segmentation. Still, current image segmentation …

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for
medical image segmentation, yet need plenty of manual annotations for training. Semi …

In recent years, there have been multiple works of literature reviewing methods for
automatically segmenting multiple sclerosis (MS) lesions. However, there is no literature …

The presence of cortical lesions in multiple sclerosis patients has emerged as an important
biomarker of the disease. They appear in the earliest stages of the illness and have been …