Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.2242
This item is licensed with a CreativeCommons licence https://creativecommons.org/licenses/by/4.0/
Publisher DOI: 10.1038/s41598-019-42294-8
Title: Comparison of deep learning approaches for multi-label chest X-ray classification
Language: English
Authors: Baltruschat, Ivo-Matteo 
Nickisch, Hannes 
Grass, Michael 
Knopp, Tobias 
Saalbach, Axel 
Issue Date: 23-Apr-2019
Publisher: Macmillan Publishers Limited, part of Springer Nature
Source: Scientific reports 1 (9): 6381 (2019-04-23)
Journal or Series Name: Scientific reports 
Abstract (english): The increased availability of labeled X-ray image archives (e.g. ChestX-ray14 dataset) has triggered a growing interest in deep learning techniques. To provide better insight into the different approaches, and their applications to chest X-ray classification, we investigate a powerful network architecture in detail: the ResNet-50. Building on prior work in this domain, we consider transfer learning with and without fine-tuning as well as the training of a dedicated X-ray network from scratch. To leverage the high spatial resolution of X-ray data, we also include an extended ResNet-50 architecture, and a network integrating non-image data (patient age, gender and acquisition type) in the classification process. In a concluding experiment, we also investigate multiple ResNet depths (i.e. ResNet-38 and ResNet-101). In a systematic evaluation, using 5-fold re-sampling and a multi-label loss function, we compare the performance of the different approaches for pathology classification by ROC statistics and analyze differences between the classifiers using rank correlation. Overall, we observe a considerable spread in the achieved performance and conclude that the X-ray-specific ResNet-38, integrating non-image data yields the best overall results. Furthermore, class activation maps are used to understand the classification process, and a detailed analysis of the impact of non-image features is provided.
URI: http://hdl.handle.net/11420/2636
DOI: 10.15480/882.2242
ISSN: 2045-2322
Institute: Biomedizinische Bildgebung E-5 
Type: (wissenschaftlicher) Artikel
Appears in Collections:Publications (tub.dok)

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