Comparison of deep learning approaches for multi-label chest X-ray classification

Link:
Autor/in:
Verlag/Körperschaft:
Hamburg University of Technology
Erscheinungsjahr:
2019
Medientyp:
Text
Schlagworte:
  • 570: Biowissenschaften, Biologie
  • 620: Ingenieurwissenschaften
  • 570
  • 620
Beschreibung:
  • 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.
Beziehungen:
DOI 10.1038/s41598-019-42294-8
Lizenzen:
  • info:eu-repo/semantics/openAccess
  • https://creativecommons.org/licenses/by/4.0/
Quellsystem:
TUHH Open Research

Interne Metadaten
Quelldatensatz
oai:tore.tuhh.de:11420/2636