The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

The comprehensive integration of machine learning healthcare models within clinical
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …

Semi-supervised learning relaxes the need of large pixel-wise labeled datasets for image
segmentation by leveraging unlabeled data. A prominent way to exploit unlabeled data is to …

Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer
from low reliability and robustness. Uncertainty estimation is an efficient solution to this …

Data augmentation is widely applied to medical image analysis tasks in limited datasets with
imbalanced classes and insufficient annotations. However, traditional augmentation …

Segmenting brain tumors in Magnetic Resonance Imaging (MRI) volumes is challenging
due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have …

Although deep learning (DL) models have shown great success in many medical image
analysis tasks, deployment of the resulting models into real clinical contexts requires:(1) that …

MR imaging is central to the assessment of tumor burden and changes over time in neuro-
oncology. Several response assessment guidelines have been set forth by the Response …

Technological advancements have enabled the extended investigation, development, and
application of computational approaches in various domains, including health care. A …

Brain tumors are usually fatal diseases with low life expectancies due to the organs they
affect, even if the tumors are benign. Diagnosis and treatment of these tumors are …