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@ARTICLE{Strack:285099,
      author       = {C. Strack$^*$ and K. L. Pomykala and H.-P. Schlemmer$^*$
                      and J. Egger and J. Kleesiek$^*$},
      title        = {'{A} net for everyone': fully personalized and unsupervised
                      neural networks trained with longitudinal data from a single
                      patient.},
      journal      = {BMC medical imaging},
      volume       = {23},
      number       = {1},
      issn         = {1471-2342},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {DKFZ-2023-02223},
      pages        = {174},
      year         = {2023},
      note         = {#EA:E010#},
      abstract     = {With the rise in importance of personalized medicine and
                      deep learning, we combine the two to create personalized
                      neural networks. The aim of the study is to show a proof of
                      concept that data from just one patient can be used to train
                      deep neural networks to detect tumor progression in
                      longitudinal datasets.Two datasets with 64 scans from 32
                      patients with glioblastoma multiforme (GBM) were evaluated
                      in this study. The contrast-enhanced T1w sequences of brain
                      magnetic resonance imaging (MRI) images were used. We
                      trained a neural network for each patient using just two
                      scans from different timepoints to map the difference
                      between the images. The change in tumor volume can be
                      calculated with this map. The neural networks were a form of
                      a Wasserstein-GAN (generative adversarial network), an
                      unsupervised learning architecture. The combination of data
                      augmentation and the network architecture allowed us to skip
                      the co-registration of the images. Furthermore, no
                      additional training data, pre-training of the networks or
                      any (manual) annotations are necessary.The model achieved an
                      AUC-score of 0.87 for tumor change. We also introduced a
                      modified RANO criteria, for which an accuracy of $66\%$ can
                      be achieved.We show a novel approach to deep learning in
                      using data from just one patient to train deep neural
                      networks to monitor tumor change. Using two different
                      datasets to evaluate the results shows the potential to
                      generalize the method.},
      keywords     = {Brain Tumor (Other) / Longitudinal (Other) / MRI (Other) /
                      Machine learning (Other) / Neural networks (Other) /
                      Personalized (Other) / Privacy-safe (Other) / Unsupervised
                      (Other) / Wasserstein-GAN (Other) / Zero-training data
                      (Other)},
      cin          = {E010 / HD01},
      ddc          = {610},
      cid          = {I:(DE-He78)E010-20160331 / I:(DE-He78)HD01-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37907876},
      doi          = {10.1186/s12880-023-01128-w},
      url          = {https://inrepo02.dkfz.de/record/285099},
}