模式识别(心理学)
深度学习
卷积神经网络
人工神经网络
生成对抗网络
监督学习
对抗制
判别式
作者
Michael Gadermayr,Laxmi Gupta,Vitus Appel,Peter Boor,Barbara M. Klinkhammer,Dorit Merhof
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2019-02-14
卷期号:38 (10): 2293-2302
被引量:36
标识
DOI:10.1109/tmi.2019.2899364
摘要
A major challenge in the field of segmentation in digital pathology is given by the high effort for manual data annotations in combination with many sources introducing variability in the image domain. This requires methods that are able to cope with variability without requiring to annotate a large amount of samples for each characteristic. In this paper, we develop approaches based on adversarial models for image-to-image translation relying on unpaired training. Specifically, we propose approaches for stain-independent supervised segmentation relying on image-to-image translation for obtaining an intermediate representation. Furthermore, we develop a fully-unsupervised segmentation approach exploiting image-to-image translation to convert from the image to the label domain. Finally, both approaches are combined to obtain optimum performance in unsupervised segmentation independent of the characteristics of the underlying stain. Experiments on patches showing kidney histology proof that stain-translation can be performed highly effectively and can be used for domain adaptation to obtain independence of the underlying stain. It is even capable of facilitating the underlying segmentation task, thereby boosting the accuracy if an appropriate intermediate stain is selected. Combining domain adaptation with unsupervised segmentation finally showed the most significant improvements.
科研通智能强力驱动
Strongly Powered by AbleSci AI