The detection of bone lesions is important for the diagnosis and staging of multiple myeloma patients. The scarce availability of annotated data renders training of automated detectors challenging. Here, we present a transfer learning approach using convolutional neural networks to detect bone lesions in computed tomography imaging data. We compare different learning approaches, and demonstrate that pretraining a convolutional neural network on natural images improves detection accuracy. Also, we describe a patch extraction strategy which encodes different information into each input channel of the networks. We train and evaluate our approach on a dataset with 660 annotated bone lesions, and show how the resulting marker map high-lights lesions in computed tomography imaging data.
CITATION STYLE
Perkonigg, M., Hofmanninger, J., Menze, B., Weber, M. A., & Langs, G. (2018). Detecting bone lesions in multiple myeloma patients using transfer learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11076 LNCS, pp. 22–30). Springer Verlag. https://doi.org/10.1007/978-3-030-00807-9_3
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