Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; 6th International Wo Alessandro Crimi,Spyridon Bakas Conferen

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Multivariate Analysis is Sufficient for Lesion-Behaviour Mappingariate methods are sufficient to address lesion-anatomical bias. This is a commonly encountered situation when working with public datasets, which very often lack general health data. We support our claim with a set of simulated experiments using a publicly available lesion imaging dataset, on which

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Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRIe segmentation performance. Our results suggest that a network trained using curriculum learning is effective at compensating for different levels of motion artifacts, and improved the segmentation performance by .9%–15% (.) when compared against a conventional shuffled learning strategy on the same

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Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regressioneen growing. Preoperative structural multi-parametric MRI (.) scans from . subjects of the TCGA-GBM imaging collection are used to quantitatively evaluate our approach. We consider the mpMRI intensities within the region defined by the abnormal FLAIR signal envelope for training one DL model for eac

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Glioma Diagnosis and Classification: Illuminating the Gold Standardolecular features, in the context of imaging and demographic information. This paper will introduce classic histologic features of gliomas in contrast to nonneoplastic brain parenchyma, describe the basic clinical algorithm used to classify infiltrating gliomas, and demonstrate how the classificatio

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Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based Methodses in a variety of medical image analysis applications has renewed community interest in this challenging problem and led to a burst of activity for new algorithm development. In this survey, we investigate the supervised CNN-based methods for MS lesion segmentation. We decouple these reviewed works

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Computational Diagnostics of GBM Tumors in the Era of Radiomics and Radiogenomicsdiogenomics. This has raised hopes for developing non-invasive and in-vivo biomarkers for prediction of patient survival, tumor recurrence, or molecular characterization, and therefore, encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-opera