Computer Assisted Medical Interventions

Uncertainty quantification

Building trust in Artificial Intelligence (AI) is a key for bringing machine learning - based solutions into clinical practice. Our division therefore puts a strong focus on the quantification and compensation of uncertainties related to the proposed image analysis methods. While we have a long history of uncertainty handling in general (see e.g. [Maier-Hein2012, Maier-Hein2016, Moccia2018]) our current research is directed to out-of-distribution (OoD) detection and the systematic handling of ambiguities in inverse problems.


Out of distribution detection

A common criticism of machine learning-based solutions is the way anomalies are handled. If a measurement is "out of distribution" (meaning that it does not resemble the training data), the algorithm cannot make a meaningful inference, and the probability of failure (error) is high. In our research, we address this epistemic uncertainty with an information theoretic approach based on the widely applicable information criterion (WAIC) [Adler2019b, Adler2019c]. This approach heavily relies upon recent methodology related to invertible neural networks (INNs) [Ardizzone2019] as their tractable Jacobian only allows us to compute WAIC.

Uncertainty handling in inverse problems

While a lot of research has been dedicated to addressing uncertainty related to the potential intrinsic randomness of the data generation process (aleatoric uncertainty) as well as to insufficient training data (epistemic uncertainty), a type of uncertainty that has received very little attention in the literature is the potential inherent ambiguity of the problem. The field of biophotonics, for example, has put a research focus onto converting high-dimensional multispectral measurements to underlying clinically relevant tissue properties, such as tissue oxygenation (see [Wirkert2016, Wirkert2017] and Fig. 1). However, state-of-the-art approaches to multispectral image interpretation typically provide point estimates and neglect the fact that the problem may be inherently ambiguous. Consequently, the estimations cannot generally be trusted to be close to the ground truth. In a joint project with the Heidelberg Institute for Scientific Computing (IWR), we addressed this problem by applying invertible neural networks (INNs) [Ardizzone2019, Adler2019a] which are powerful enough to generate full probability distributions for the predicted value compared to point estimates. We have shown that for a small number of bands these probability distributions become increasingly ambiguous to the point where they start to develop so called multiple modes which makes an inversion of this problem ill-posed and so nigh impossible.

Key collaborators

  • Prof. Dr. Carsten Rother, Visual Learning Lab, Heidelberg University
  • Prof. Dr. Ullrich Köthe, Visual Learning Lab, Heidelberg University
  • Prof. Dr. Dogu Teber, Urological Clinic, Municipal Clinic Karlsruhe
  • Prof. Dr. Beat P. Müller-Stich, Section of Minimally-invasive Surgery of the Department of General Surgery, Heidelberg University
  • Dr. Hannes G. Kenngott, Section of Minimally-invasive Surgery of the Department of General Surgery, Heidelberg University
  • Dr. Edgar Santos, University Clinic for Neurosurgery Heidelberg, University Clinic Heidelberg


Adler, T.J., Ardizzone, L., Vemuri, A., Ayala, L. Gröhl, J., Kirchner, T., Wirkert, S., Kruse, J., Rother, C., Köthe, U. and Maier-Hein, L., "Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks." International journal of computer assisted radiology and surgery (IJCARS, 2019)

Adler, T. J., Ardizzone, L., Ayala, L., Gröhl, J., Vemuri, A., Wirkert, S. J., Müller-Stich B. P., Rother C., Köthe U. and Maier-Hein, L., Uncertainty handling in intra-operative multispectral imaging with invertible neural networks. Medical Imaging with Deep Learning (MIDL, 2019)

Adler, T.J., Ayala, L., Ardizzone, L., Kenngott, H.G., Vemuri, A., Müller-Stich, B.P., Rother, C., Köthe, U. and Maier-Hein, L., Out of distribution detection for intra-operative functional imaging. In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures (MICCAI-UNSURE). Springer, Cham. (2019)

Ardizzone, L., Kruse, J., Wirkert, S., Rahner, D., Pellegrini, E.W., Klessen, R.S., Maier-Hein, L., Rother, C. and Köthe, U., Analyzing inverse problems with invertible neural networks. International Conference on Learning Representations (ICLR, 2019)

Gröhl, J., Kirchner, T., Adler, T.J., and Maier-Hein, L. Confidence estimation for machine learning-based quantitative photoacoustics. Journal of Imaging (2018)

Wirkert, S.J., Vemuri, A.S., Kenngott, H.G., Moccia, S., Götz, M., Mayer, B.F., Maier-Hein, K.H., Elson, D.S. and Maier-Hein, L., Physiological parameter estimation from multispectral images unleashed. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Springer, Cham. (2017)

Moccia, S., Wirkert, S.J., Kenngott, H., Vemuri, A.S., Apitz, M., Mayer, B., De Momi, E., Mattos, L.S. and Maier-Hein, L., Uncertainty-aware organ classification for surgical data science applications in laparoscopy. IEEE Transactions on Biomedical Engineering (2018)

Maier-Hein, L., Ross, T., Gröhl, J., Glocker, B., Bodenstedt, S., Stock, C., Heim, E., Götz, M., Wirkert, S., Kenngott, H. and Speidel, S., Crowd-algorithm collaboration for large-scale endoscopic image annotation with confidence. In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Springer, Cham. (2016)

Wirkert, S.J., Kenngott, H., Mayer, B., Mietkowski, P., Wagner, M., Sauer, P., Clancy, N.T., Elson, D.S. and Maier-Hein, L., Robust near real-time estimation of physiological parameters from megapixel multispectral images with inverse Monte Carlo and random forest regression. International journal of computer assisted radiology and surgery (IJCARS, 2016)

Maier-Hein, L., Franz, A.M., Dos Santos, T.R., Schmidt, M., Fangerau, M., Meinzer, H.P. and Fitzpatrick, J.M., Convergent iterative closest-point algorithm to accommodate anisotropic and inhomogeneous localization error. IEEE transactions on pattern analysis and machine intelligence (PAMI, 2012)

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