Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.
about
Deep reinforcement learning for automated radiation adaptation in lung cancer.Radiomics and radiogenomics for precision radiotherapy.Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks.Deep Deconvolutional Neural Network for Target Segmentation of Nasopharyngeal Cancer in Planning Computed Tomography Images.Strategies for prediction and mitigation of radiation-induced liver toxicity.Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning.Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs.Evaluation of deep learning methods for parotid gland segmentation from CT imagesMachine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges
P2860
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P2860
Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.
description
2017 nî lūn-bûn
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2017年の論文
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2017年学术文章
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2017年学术文章
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2017年学术文章
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2017年学术文章
@zh-hans
2017年学术文章
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2017年学术文章
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2017年學術文章
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2017年學術文章
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name
Segmentation of organs-at-risk ...... convolutional neural networks.
@en
Segmentation of organs-at-risk ...... convolutional neural networks.
@nl
type
label
Segmentation of organs-at-risk ...... convolutional neural networks.
@en
Segmentation of organs-at-risk ...... convolutional neural networks.
@nl
prefLabel
Segmentation of organs-at-risk ...... convolutional neural networks.
@en
Segmentation of organs-at-risk ...... convolutional neural networks.
@nl
P2860
P356
P1433
P1476
Segmentation of organs-at-risk ...... convolutional neural networks.
@en
P2093
Bulat Ibragimov
P2860
P304
P356
10.1002/MP.12045
P407
P577
2017-02-01T00:00:00Z