@article{RohrbachReinhardSicketal.2019,
  author    = {Rohrbach, Janick and Reinhard, Tobias and Sick, Beate and D{\"u}rr, Oliver},
  title     = {Bone erosion scoring for rheumatoid arthritis with deep convolutional neural networks},
  journal   = {Computers \& Electrical Engineering},
  volume    = {Vol. 78},
  issn      = {0045-7906},
  doi       = {10.1016/j.compeleceng.2019.08.003},
  institution = {Institut f{\"u}r Optische Systeme - IOS},
  pages     = {472 -- 481},
  year      = {2019},
  abstract  = {Rheumatoid arthritis is an autoimmune disease that causes chronic inflammation of synovial joints, often resulting in irreversible structural damage. The activity of the disease is evaluated by clinical examinations, laboratory tests, and patient self-assessment. The long-term course of the disease is assessed with radiographs of hands and feet. The evaluation of the X-ray images performed by trained medical staff requires several minutes per patient. We demonstrate that deep convolutional neural networks can be leveraged for a fully automated, fast, and reproducible scoring of X-ray images of patients with rheumatoid arthritis. A comparison of the predictions of different human experts and our deep learning system shows that there is no significant difference in the performance of human experts and our deep learning model.},
  language  = {en}
}