A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning.
Mikael Agn,Per Munck af Rosenschöld,Oula Puonti,Michael Lundemann,Laura Mancini,Anastasia Papadaki,Steffi Thust,John Ashburner,Ian Law,Koen Van Leemput +9 more
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In this paper, a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines is presented.About:
This article is published in Medical Image Analysis.The article was published on 2019-05-01 and is currently open access. It has received 35 citations till now. The article focuses on the topics: Radiation treatment planning & Radiation therapy.read more
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Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation.
TL;DR: In this paper, a review of major statistical and deep learning models and their applications in brain imaging research with a focus on MRI-based brain tumor segmentation is presented, highlighting that model-driven classical statistics and data-driven deep learning is a potent combination for developing automated systems in clinical oncology.
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Методы машинного обучения в сегментации глиом для планирования стереотаксической лучевой терапии
TL;DR: This review aims to summarize recent works using machine learning in high — and low — grade glioma segmentation in multimodal MRI imaging.
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Learning Tumor-Induced Deformations to Improve Tumor-Bearing Brain MR Segmentation
M. Jia,Matthew Kyan +1 more
TL;DR: In this article , a point-cloud deep learning method is used to predict a displacement field, which is meant to be the deformation (inverse) caused by the growth (masseffect and cell-infiltration) of the tumor.
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Developing an AI-assisted planning pipeline for hippocampal avoidance whole brain radiotherapy.
Chih Yuan Lin,Lin Shan Chou,Yuan-Hung Wu,John S. Kuo,Minesh P. Mehta,An Cheng Shiau,Jian Liang,Shih Ming Hsu,Ti Hao Wang +8 more
TL;DR: In this article , a pipeline using deep learning tools for a fully automated treatment planning workflow to generate HA-WBRT radiotherapy plans was designed and evaluated using RTOG-0933 clinical trial protocol guidelines, all organs at risk and the clinical target volume (CTV) were contoured by experienced radiation oncologists.
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Deep-learning magnetic resonance imaging-based automatic segmentation for organs-at-risk in the brain: Accuracy and impact on dose distribution
A Turcas,Daniel Corneliu Leucuta,Enrico Clementel,Cristina Gheara,Alex Cristian Kacsó,Sarah M. Kelly,Dana Cernea,Patriciu Achimas-Cadariu +7 more
TL;DR: In this paper , a commercial, deep-learning, MRI-based tool for brain organs-at-risk delineation was assessed, and two additional structure sets were obtained: AI (artificial intelligence) and AIedit (manually corrected auto-contours).
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Proceedings Article
Auto-Encoding Variational Bayes
Diederik P. Kingma,Max Welling +1 more
TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.
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Machine Learning : A Probabilistic Perspective
TL;DR: This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach, and is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
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On the limited memory BFGS method for large scale optimization
Dong C. Liu,Jorge Nocedal +1 more
TL;DR: The numerical tests indicate that the L-BFGS method is faster than the method of Buckley and LeNir, and is better able to use additional storage to accelerate convergence, and the convergence properties are studied to prove global convergence on uniformly convex problems.
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Training products of experts by minimizing contrastive divergence
TL;DR: A product of experts (PoE) is an interesting candidate for a perceptual system in which rapid inference is vital and generation is unnecessary because it is hard even to approximate the derivatives of the renormalization term in the combination rule.