AIMC Topic: Brain Neoplasms

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Enhanced Regularized Ensemble Encoderdecoder Network for Accurate Brain Tumor Segmentation.

Current medical imaging
BACKGROUND: Segmenting tumors in MRI scans is a difficult and time-consuming task for radiologists. This is because tumors come in different shapes, sizes, and textures, making them hard to identify visually.

Development and validation of a clinical prediction model for glioma grade using machine learning.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Histopathological evaluation is currently the gold standard for grading gliomas; however, this technique is invasive.

Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation.

Radiology. Artificial intelligence
Purpose To present results from a literature survey on practices in deep learning segmentation algorithm evaluation and perform a study on expert quality perception of brain tumor segmentation. Materials and Methods A total of 180 articles reporting ...

Classification of Brain Tumours in MRI Images using a Convolutional Neural Network.

Current medical imaging
INTRODUCTION: Recent advances in deep learning have aided the well-being business in Medical Imaging of numerous disorders like brain tumours, a serious malignancy caused by unregulated and aberrant cell portioning. The most frequent and widely used ...

Whole-brain radiotherapy associated with structural changes resembling aging as determined by anatomic surface-based deep learning.

Neuro-oncology
BACKGROUND: Brain metastases are the most common intracranial tumors in adults and are associated with significant morbidity and mortality. Whole-brain radiotherapy (WBRT) is used frequently in patients for palliation, but can result in neurocognitiv...

A Deep Learning Framework for Skull Stripping in Brain MRI.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Skull-stripping, an important pre-processing step in neuroimage computing, involves the automated removal of non-brain anatomy (such as the skull, eyes, and ears) from brain images to facilitate brain segmentation and analysis. Manual segmentation is...

Interhemispheric connections in the maintenance of language performance and prognosis prediction: fully connected layer-based deep learning model analysis.

Neurosurgical focus
OBJECTIVE: Language-related networks have been recognized in functional maintenance, which has also been considered the mechanism of plasticity and reorganization in patients with cerebral malignant tumors. However, the role of interhemispheric conne...

Code-free machine learning for classification of central nervous system histopathology images.

Journal of neuropathology and experimental neurology
Machine learning (ML), an application of artificial intelligence, is currently transforming the analysis of biomedical data and specifically of biomedical images including histopathology. The promises of this technology contrast, however, with its cu...

Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning.

Neuro-oncology
BACKGROUND: Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is time-consuming. Previously, deep learning methods have been developed that can...

Review on the Artificial Intelligence-based Nanorobotics Targeted Drug Delivery System for Brain-specific Targeting.

Current pharmaceutical design
Contemporary medical research increasingly focuses on the blood-brain barrier (BBB) to maintain homeostasis in healthy individuals and provide solutions for neurological disorders, including brain cancer. Specialized in vitro modules replicate the BB...