% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@MISC{Pfaehler:1032397,
      author       = {Pfaehler, Elisabeth and Krieger, Lena},
      title        = {{E}xplainable {AI} in {M}edical {I}mage {A}nalysis},
      reportid     = {FZJ-2024-06207},
      year         = {2024},
      note         = {Elisabeth Pfaehler is funded by the European Union,
                      Marie-Curie Sklodowska Fellowship HORIZON-MSCA-2021-PF-01,
                      grant 101068572.},
      abstract     = {The use of Artificial Intelligence (AI) in the medical
                      domain is of high interest. AI could facilitate the work of
                      physicians and guide them in their clinical decision-making.
                      However, many AI-based methods are still a black box and
                      hardly understood. As every patient has the right to an
                      explainable diagnosis, it is important to understand and
                      explain the processes and reasons behind the decisions of
                      Convolutional Neural Networks (CNNs). In this workshop, we
                      will explain the different applications of AI and
                      Explainable AI in medical image analysis. The participants
                      will learn about the different Explainable AI methods, their
                      limitations, and how they could be included in a clinical
                      workflow. Some Explainable AI methods will be applied to
                      examples.},
      month         = {Aug},
      date          = {2024-08-19},
      organization  = {Informatica Feminale, (Germany), 19
                       Aug 2024 - 21 Aug 2024},
      subtyp        = {Other},
      cin          = {IAS-8 / INM-4},
      cid          = {I:(DE-Juel1)IAS-8-20210421 / I:(DE-Juel1)INM-4-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)17},
      url          = {https://juser.fz-juelich.de/record/1032397},
}