Data augmentation is a popular technique which helps improve generalization capabilities
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …

Despite the overall success of using artificial intelligence (AI) to assist radiologists in
performing computer-aided patient diagnosis, it remains challenging to build good models …

Semantic segmentation of brain tumors is a fundamental medical image analysis task
involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient …

The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the
Radiological Society of North America (RSNA), the American Society of Neuroradiology …

Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard
and important tasks for several applications in the field of medical analysis. As each brain …

Brain tumor semantic segmentation is a critical medical image processing work, which aids
clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural …

In the past decade, deep neural networks, particularly convolutional neural networks, have
revolutionised computer vision. However, all deep learning models may require a large …

Several studies underscore the potential of deep learning in identifying complex patterns,
leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse …

We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The unmodified
nnU-Net baseline configuration already achieves a respectable result. By incorporating …

We propose a Transformer architecture for volumetric segmentation, a challenging task that
requires keeping a complex balance in encoding local and global spatial cues, and …