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Erschienen in:
HEp-2 Cell Classification Using K-Support Spatial Pooling in Deep CNNs Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Understanding the Mechanisms of Deep Transfer Learning for Medical Images

verfasst von : Hariharan Ravishankar, Prasad Sudhakar, Rahul Venkataramani, Sheshadri Thiruvenkadam, Pavan Annangi, Narayanan Babu, Vivek Vaidya

Erschienen in: Deep Learning and Data Labeling for Medical Applications

Verlag: Springer International Publishing

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Abstract

The ability to automatically learn task specific feature representations has led to a huge success of deep learning methods. When large training data is scarce, such as in medical imaging problems, transfer learning has been very effective. In this paper, we systematically investigate the process of transferring a Convolutional Neural Network, trained on ImageNet images to perform image classification, to kidney detection problem in ultrasound images. We study how the detection performance depends on the extent of transfer. We show that a transferred and tuned CNN can outperform a state-of-the-art feature engineered pipeline and a hybridization of these two techniques achieves 20 % higher performance. We also investigate how the evolution of intermediate response images from our network. Finally, we compare these responses to state-of-the-art image processing filters in order to gain greater insight into how transfer learning is able to effectively manage widely varying imaging regimes.

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Vorheriges Kapitel The Importance of Skip Connections in Biomedical Image Segmentation
Nächstes Kapitel A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography
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Metadaten
Titel
Understanding the Mechanisms of Deep Transfer Learning for Medical Images
verfasst von
Hariharan Ravishankar
Prasad Sudhakar
Rahul Venkataramani
Sheshadri Thiruvenkadam
Pavan Annangi
Narayanan Babu
Vivek Vaidya
Copyright-Jahr
2016
Buch
Deep Learning and Data Labeling for Medical Applications

Print ISBN: 978-3-319-46975-1

Electronic ISBN: 978-3-319-46976-8

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46976-8_20