Brain tumor imaging using Machine Learning.

When imaging brain tumors such as gliomas, machine learning may advance the use of imaging and augment clinical care for patients.Although there is great potential for improving clinical diagnostics, prognostics and decision-making with machine .To overcome future challenges, researchers believe characterizing brain tumors through more stable, reproducible automated methods can help gather more quantitative data to better assess, define and characterize tumors.Advances in convolutional neural network (CNN) deep learning architectures for tumor segmentation are contributing to the progress of MRI radiomics. Deep learning algorithms can be stacked to achieve end-to-end training from image to segmentation to classification to outcome prediction. Deep learning is capable of learning highly intricate and abstract patterns from multimodal imaging that may not be readily apparent to observers. This method allows the underpowered task-in-hand to borrow statistical power,while still being fine-tuned for the task-in-hand.Recent work in visualization of deep learning models is, however, providing some avenues for opening the black-box and visualizing these biomarkers. 

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