Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis,
treatment planning, and treatment outcome evaluation. Build upon successful deep learning …

Deep learning approaches such as convolutional neural nets have consistently
outperformed previous methods on challenging tasks such as dense, semantic …

Deep neural networks and decision trees operate on largely separate paradigms; typically,
the former performs representation learning with pre-specified architectures, while the latter …

Background Image segmentation is an essential step in the analysis and subsequent
characterisation of brain tumours through magnetic resonance imaging. In the literature …

Traditional deep convolutional neural networks for fully automatic brain tumor segmentation
have two problems: spatial information loss caused by both the repeated pooling/striding …

Among the currently proposed brain segmentation methods, brain tumor segmentation
methods based on traditional image processing and machine learning are not ideal enough …

Objectives Medical image analysis practices face challenges that can potentially be
addressed with algorithm-based segmentation tools. In this study, we map the field of …

Deep neural networks have been proved a success in multiple fields. However, researchers
still favor traditional approaches to obtain more interpretable models, such as Bayesian …

We present an efficient deep learning approach for the challenging task of tumor
segmentation in multisequence MR images. In recent years, Convolutional Neural Networks …

Segmentation of brain lesions from magnetic resonance images (MRI) is an important step
for disease diagnosis, surgical planning, radiotherapy and chemotherapy. However, due to …