Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation

@inproceedings{Vugt2019VendorindependentST,
  title={Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation},
  author={J. V. Vugt and E. Marchiori and R. Mann and A. Gubern-M{\'e}rida and N. Moriakov and Jonas Teuwen},
  booktitle={Medical Imaging},
  year={2019}
}
  • J. V. Vugt, E. Marchiori, +3 authors Jonas Teuwen
  • Published in Medical Imaging 2019
  • Computer Science, Engineering
  • Computer-aided detection aims to improve breast cancer screening programs by helping radiologists to evaluate digital mammography (DM) exams. DM exams are generated by devices from different vendors, with diverse characteristics between and even within vendors. Physical properties of these devices and postprocessing of the images can greatly influence the resulting mammogram. This results in the fact that a deep learning model trained on data from one vendor cannot readily be applied to data… CONTINUE READING

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