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2019 | OriginalPaper | Buchkapitel

7. Thoracic Disease Identification and Localization with Limited Supervision

verfasst von : Zhe Li, Chong Wang, Mei Han, Yuan Xue, Wei Wei, Li-Jia Li, Li Fei-Fei

Erschienen in: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics

Verlag: Springer International Publishing

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Abstract

Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning. Building a highly accurate prediction model for these tasks usually requires a large number of images manually annotated with labels and finding sites of abnormalities. In reality, however, such annotated data are expensive to acquire, especially the ones with location annotations. We need methods that can work well with only a small amount of location annotations. To address this challenge, we present a unified approach that simultaneously performs disease identification and localization through the same underlying model for all images. We demonstrate that our approach can effectively leverage both class information as well as limited location annotation, and significantly outperforms the comparative reference baseline in both classification and localization tasks.

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Nächstes Kapitel Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI

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Titel: Thoracic Disease Identification and Localization with Limited Supervision
verfasst von: Zhe Li
Chong Wang
Mei Han
Yuan Xue
Wei Wei
Li-Jia Li
Li Fei-Fei
Verlag: Springer International Publishing
Buch: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
Print ISBN: 978-3-030-13968-1

Electronic ISBN: 978-3-030-13969-8

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-13969-8_7

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