Weighting training images by maximizing distribution similarity for supervised segmentation across scanners

van Opbroek Annegreet; Vernooij Meike W.; Ikram M. Arfan; de Bruijne Marleen

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Weighting training images by maximizing distribution similarity for supervised segmentation across scanners

机译：通过最大化分布相似度对训练图像加权，以实现跨扫描器的有监督分割

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Many automatic segmentation methods are based on supervised machine learning. Such methods have proven to perform well, on the condition that they are trained on a sufficiently large manually labeled training set that is representative of the images to segment. However, due to differences between scanners, scanning parameters, and patients such a training set may be difficult to obtain.

机译：许多自动分割方法都是基于监督的机器学习。在以足够大的，手动标记的训练集（代表要分割的图像）进行训练的条件下，已证明此类方法可以很好地执行。但是，由于扫描仪，扫描参数和患者之间的差异，这样的训练集可能很难获得。

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《Medical image analysis》 |2015年第1期|共10页
作者
van Opbroek Annegreet; Vernooij Meike W.; Ikram M. Arfan; de Bruijne Marleen;
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正文语种 eng
中图分类影像诊断学;
关键词
Brain; Domain adaptation; Machine learning; MRI; Transfer learning;

机译：脑;域适应;机器学习;MRI;转移学习;

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专利

1. Weighting training images by maximizing distribution similarity for supervised segmentation across scanners [J] . van Opbroek Annegreet, Vernooij Meike W., Ikram M. Arfan, Medical image analysis . 2015,第1期

机译：通过最大化分布相似度对训练图像加权，以实现跨扫描器的有监督分割
2. Un-supervised and semi-supervised hand segmentation in egocentric images with noisy label learning [J] . Li Yinlin, Jia Lihao, Wang Zidong, Neurocomputing . 2019,第MARa21期

机译：带有噪声标签学习的自我中心图像中的无监督和半监督手分割
3. Weakly Supervised Learning with Deep Convolutional Neural Networks for Semantic Segmentation: Understanding Semantic Layout of Images with Minimum Human Supervision [J] . Seunghoon Hong, Suha Kwak, Bohyung Han IEEE Signal Processing Magazine . 2017,第6期

机译：使用深度卷积神经网络进行语义监督的弱监督学习：以最少的人工监督了解图像的语义布局
4. A Transfer-Learning Approach to Image Segmentation Across Scanners by Maximizing Distribution Similarity [C] . Annegreet van Opbroek, M. Arfan Ikram, Meike W. Vernooij, Machine learning in medical imaging . 2013

机译：通过最大化分布相似度的跨学习机图像分割的转移学习方法
5. Maximizing the Data Utilization Efficiency in Medical Imaging Diagnosis: From Full Supervision to Weak Supervision [D] . Li, Xiaomeng. 2019

机译：最大化医学成像诊断的数据利用效率：从全面监督到弱势监督
6. ANATOMICAL GUIDED SEGMENTATION WITH NON-STATIONARY TISSUE CLASS DISTRIBUTIONS IN AN EXPECTATION-MAXIMIZATION FRAMEWORK [O] . Kilian M. Pohl, Sylvain Bouix, Ron Kikinis, -1

机译：期望最大化框架中具有非平稳组织类分布的解剖学指导的分割
7. Multi-atlas label fusion by using supervised local weighting for brain image segmentation [O] . David Cárdenas-Peña, Eduardo Fernández, José M. Ferrández-Vicente, 2017

机译：多标准标签融合通过使用脑图像分割的监督局部加权

Weighting training images by maximizing distribution similarity for supervised segmentation across scanners

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