Divide and Conquer: Stratifying Training Data by Tumor Grade Improves Deep Learning-Based Brain Tumor Segmentation
نویسندگان
چکیده
منابع مشابه
Divide-and-Conquer Subspace Segmentation
Several important computer vision tasks have recently been formulated as lowrank problems, with the Low-Rank Representation method (LRR) being one recent and prominent formulation. Although the method is framed as a convex program, available solutions to this program are inherently sequential and costly, thus limiting its scalability. In this work, we explore the effectiveness of a recently int...
متن کاملBrain tumor segmentation with Deep Neural Networks
In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learnin...
متن کاملDivide-and-Conquer Reinforcement Learning
Standard model-free deep reinforcement learning (RL) algorithms sample a new initial state for each trial, allowing them to optimize policies that can perform well even in highly stochastic environments. However, problems that exhibit considerable initial state variation typically produce high-variance gradient estimates for model-free RL, making direct policy or value function optimization cha...
متن کاملModel-Based Brain and Tumor Segmentation
Combining image segmentation based on statistical classification with a geometric prior has been shown to significantly increase robustness and reproducibility. Using a probabilistic geometric model of sought structures and image registration serves both initialization of probability density functions and definition of spatial constraints. A strong spatial prior, however, prevents segmentation ...
متن کاملDivide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry
Low success (<60%) in autism spectrum disorder (ASD) classification using brain morphometry from the large multi-site ABIDE dataset and inconsistent findings on brain morphometric abnormalities in ASD can be attributed to the ASD heterogeneity. In this study, we show that ASD brain morphometry is highly heterogeneous, and demonstrate that the heterogeneity can be mitigated and classification im...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Frontiers in Neuroscience
سال: 2019
ISSN: 1662-453X
DOI: 10.3389/fnins.2019.01182