Baltruschat, Ivo M.Ivo M.BaltruschatNickisch, HannesHannesNickischGrass, MichaelMichaelGrassKnopp, TobiasTobiasKnoppSaalbach, AxelAxelSaalbach2019-05-072019-05-072019-04-23Scientific reports 1 (9): 6381 (2019-04-23)http://hdl.handle.net/11420/2636The 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.en2045-232220191Art. Nr. 6381Macmillan Publishers Limited, part of Springer Naturehttps://creativecommons.org/licenses/by/4.0/Biowissenschaften, BiologieIngenieurwissenschaftenJournal Articleurn:nbn:de:gbv:830-882.03445910.15480/882.224210.1038/s41598-019-42294-810.15480/882.2242Other