Exploració per tema "Neuro-oncology"
Ara es mostren els items 1-6 de 6
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Advances in the use of deep learning for the analysis of magnetic resonance image in neuro-oncology
(Multidisciplinary Digital Publishing Institute (MDPI), 2024-01-10)
Article
Accés obertMachine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these ... -
AI-based glioma grading for a trustworthy diagnosis: an analytical pipeline for improved reliability
(2023-06-27)
Article
Accés obertGlioma is the most common type of tumor in humans originating in the brain. According to the World Health Organization, gliomas can be graded on a four-stage scale, ranging from the most benign to the most malignant. The ... -
Bayesian semi non-negative matrix factorisation
(I6doc.com, 2016)
Text en actes de congrés
Accés obertNon-negative Matrix Factorisation (NMF) has become a standard method for source identification when data, sources and mixing coefficients are constrained to be positive-valued. The method has recently been extended to allow ... -
Classifying malignant brain tumours from 1H-MRS data using Breadth Ensemble Learning
(Institute of Electrical and Electronics Engineers (IEEE), 2012)
Text en actes de congrés
Accés obertIn neuro oncology, the accurate diagnostic identification and characterization of tumours is paramount for determining their prognosis and the adequate course of treatment. This is usually a difficult problem per se, due ... -
QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation -- analysis of ranking metrics and benchmarking results
(2021-12-19)
Report de recerca
Accés obertDeep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal ... -
QU-BraTS: MICCAI BraTS 2020 challenge on quantifying uncertainty in brain tumor segmentation: analysis of ranking scores and benchmarking results
(2022-08-26)
Article
Accés obertDeep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) chal- lenges. However, the task of focal pathology ...