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@INPROCEEDINGS{Glass:1018411,
      author       = {Glass, Torben and Schiffer, Christian and Amunts, Katrin
                      and Dickscheid, Timo},
      title        = {{O}n {U}ncertainty-aware {D}eep {L}earning for
                      {C}ytoarchitecture {C}lassification},
      reportid     = {FZJ-2023-04792},
      year         = {2023},
      abstract     = {High-resolution light-microscopic scans of histological
                      brain sections allow identifying cytoarchitectonic areas.
                      They are defined by the local characteristics of
                      microstructural organization, which encompasses the size,
                      type, shape, and distribution of neurons, as well as their
                      distinct laminar and columnar organization. As established
                      brain mapping methods relying on statistical image analysis
                      are infeasible to handle the large size of high-resolution
                      datasets acquired by high-throughput microscopic scanners,
                      recent research focused on the development of automated
                      cytoarchitecture classification methods based on deep
                      learning. While the performance of these deep learning
                      methods has steadily increased over the last years, they are
                      unable to provide reliable estimates of prediction
                      uncertainty. In particular, the softmax outputs of
                      classification networks are generally not well suited to
                      estimate a model's uncertainty. The lack of well-calibrated
                      uncertainty estimates makes the interpretation of
                      predictions challenging, in particular when dealing with
                      out-of-distribution data.To this end, we here studied the
                      behavior of a state-of-the-art deep neural network for
                      cytoarchitecture classification with respect to its
                      uncertainty awareness. We compared it to two methods for
                      uncertainty quantification: Dropout variational inference
                      (DVI), which quantifies uncertainty based on the variance of
                      multiple predictions acquired with inference-time dropout,
                      and evidential deep learning (EDL), which is explicitly
                      trained to output an informative uncertainty score. We apply
                      both methods to in-distribution test data and
                      out-of-distribution data from a brain not seen during
                      training. We compare the models based on calibration
                      metrics, uncertainty scores, and prediction entropy.Our
                      experiments revealed that the baseline model is generally
                      overconfident, an often reported behavior of neural networks
                      that manifests as high-prediction probability even for
                      incorrectly classified samples. We observe similar behavior
                      for out-of-distribution samples from a brain not included
                      during training, where the model was unable to express its
                      inability to make accurate predictions. In comparison to the
                      baseline, both DVI and EDL resulted in considerably more
                      plausible uncertainty measures. For example, we observed
                      that the uncertainty scores obtained from models trained
                      with EDL indicate high certainty in regions with highly
                      distinct cytoarchitectonic properties, including the primary
                      visual and motor cortex. While EDL outputs a single
                      normalized uncertainty score per sample, DVI provides
                      class-level uncertainty estimates based on per-class
                      variance. This allows us to obtain localized uncertainty
                      measures for specific brain regions. For example, we
                      observed a low-certainty ribbon for the primary visual
                      cortex at the transition between primary and secondary
                      visual cortex, indicating cytoarchitectonic ambiguities at
                      the boundary between the two regions.These ambiguities could
                      be linked to the complex border phenomena that are
                      characteristic of this region, the so-called border tuft and
                      fringe area.Our study revealed that predictions of existing
                      models for cytoarchitecture classification are not well
                      calibrated and lack the ability to express uncertainty. The
                      investigated methods address these issues, providing
                      complementary methods to assess uncertainty and improve
                      model calibration. Future research will focus on the
                      refinement of the training strategy and the involved
                      hyperparameters. Finally, we plan to exploit the obtained
                      uncertainty measures to identify high-certainty predictions
                      for self-training approaches, which we expect to improve
                      classification performance.},
      month         = {Oct},
      date          = {2023-10-04},
      organization  = {7th BigBrain Workshop, Reykjavík
                       (Iceland), 4 Oct 2023 - 6 Oct 2023},
      subtyp        = {After Call},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5254 - Neuroscientific Data Analytics and AI
                      (POF4-525) / HIBALL - Helmholtz International BigBrain
                      Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
                      / Helmholtz AI - Helmholtz Artificial Intelligence
                      Coordination Unit – Local Unit FZJ (E.40401.62)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5254 /
                      G:(DE-HGF)InterLabs-0015 / G:(DE-Juel-1)E.40401.62},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1018411},
}