Data augmentation for brain-tumor segmentation: a review
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 …
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
Gliomas are the most common primary brain malignancies, with different degrees of
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
TranSiam: Aggregating multi-modal visual features with locality for medical image segmentation
Automatic segmentation of medical images plays an important role in the diagnosis of
diseases. On single-modal data, convolutional neural networks have demonstrated …
diseases. On single-modal data, convolutional neural networks have demonstrated …
Automatic segmentation of glioblastoma multiform brain tumor in MRI images: Using Deeplabv3+ with pre-trained Resnet18 weights
Purpose To assess the effectiveness of deep learning algorithms in automated
segmentation of magnetic resonance brain images for determining the enhanced tumor, the …
segmentation of magnetic resonance brain images for determining the enhanced tumor, the …
[HTML][HTML] Self-supervised learning-based two-phase flow regime identification using ultrasonic sensors in an S-shape riser
Two-phase flow regime identification is an essential transdisciplinary topic that spans digital
signal processing, artificial intelligence, chemical engineering, and energy. Multiphase flow …
signal processing, artificial intelligence, chemical engineering, and energy. Multiphase flow …
Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important
role in diagnosis and grading of brain tumors. Although manual DCE biomarker extraction …
role in diagnosis and grading of brain tumors. Although manual DCE biomarker extraction …
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient
Metrics optimized in complex machine learning tasks are often selected in an ad-hoc
manner. It is unknown how they align with human expert perception. We explore the …
manner. It is unknown how they align with human expert perception. We explore the …
HMNet: Hierarchical multi-scale brain tumor segmentation network
An accurate and efficient automatic brain tumor segmentation algorithm is important for
clinical practice. In recent years, there has been much interest in automatic segmentation …
clinical practice. In recent years, there has been much interest in automatic segmentation …
Detecting liver cirrhosis in computed tomography scans using clinically-inspired and radiomic features
Hepatic cirrhosis is an increasing cause of mortality in developed countries—it is the
pathological sequela of chronic liver diseases, and the final liver fibrosis stage. Since …
pathological sequela of chronic liver diseases, and the final liver fibrosis stage. Since …
Rock segmentation in the navigation vision of the planetary rovers
Visual navigation is an essential part of planetary rover autonomy. Rock segmentation
emerged as an important interdisciplinary topic among image processing, robotics, and …
emerged as an important interdisciplinary topic among image processing, robotics, and …