Introduction: There has been a rapid development of deep learning (DL) models for medical
imaging. However, DL requires a large labeled dataset for training the models. Getting large …

Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …

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 …

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS)
patients. Segmentation of the spinal cord and lesions from MRI data provides measures of …

A deep learning model trained on some labeled data from a certain source domain
generally performs poorly on data from different target domains due to domain shifts …

Deep learning models perform best when tested on target (test) data domains whose
distribution is similar to the set of source (train) domains. However, model generalization can …

Abstract Domain Adaptation (DA) methods are widely used in medical image segmentation
tasks to tackle the problem of differently distributed train (source) and test (target) data. We …

MRI scans appearance significantly depends on scanning protocols and, consequently, the
data-collection institution. These variations between clinical sites result in dramatic drops of …

In this paper we present a method for simultaneously segmenting brain tumors and an
extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method …

Transfer learning is widely used for training machine learning models. Here, we study the
role of transfer learning for training fully convolutional networks (FCNs) for medical image …