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141.
Klinger, E. & Hasenauer, J.: A scheme for adaptive selection of population sizes in approximate Bayesian Computation - Sequential Monte Carlo. Lect. Notes Comput. Sc. 10545 LNBI, 128-144 (2017)
142.
Bortsova, G. et al.: Mitosis detection in intestinal crypt images with hough forest and conditional random fields. Lect. Notes Comput. Sc. 10019, 287-295 (2016)
143.
Loos, C. ; Fiedler, A. & Hasenauer, J.: Parameter estimation for reaction rate equation constrained mixture models.  Lect. Notes Comput. Sc. 9859, 186-200 (2016)
144.
Zhou, L.* ; Georgii, E. ; Plant, C.* & Böhm, C.*: Covariate-related structure extraction from paired data. Lect. Notes Comput. Sc. 9832, 151-162 (2016)
145.
Bergmann, R.* & Weinmann, A.: Inpainting of cycling data using first and second order differences. Lect. Notes Comput. Sc. 8932, 155-168 (2015)
146.
Derntl, A. et al.: Stroke lesion segmentation using a probabilistic atlas of cerebral vascular territories. Lect. Notes Comput. Sc. (2015)
147.
Ilmonen, P.* ; Nordhausen, K.* ; Oja, H.* & Theis, F.J.: An affine equivariant robust second-order BSS method. Lect. Notes Comput. Sc. 9237, 328-335 (2015)
148.
Loos, C. ; Marr, C. ; Theis, F.J. & Hasenauer, J.: Approximate bayesian computation for stochastic single-cell time-lapse data using multivariate test statistics. Lect. Notes Comput. Sc. 9308, 52-63 (2015)
149.
Peter, L.* et al.: Leveraging random forests for interactive exploration of large histological images. Lect. Notes Comput. Sc. 8673, 1-8 (2015)
150.
Peter, L.* ; Pauly, O. ; Chatelain, P.* ; Mateus, D. & Navab, N.*: Scale-adaptive forest training via an efficient feature sampling scheme. Lect. Notes Comput. Sc. 9349, 637-644 (2015)
151.
Schröder, K.: Error estimates for approximate operator inversion via Kernel-based methods. Lect. Notes Comput. Sc. 9213, 399-413 (2015)
152.
Zweng, M.* et al.: Automatic guide-wire detection for neurointerventions using low-rank sparse matrix decomposition and denoising. Lect. Notes Comput. Sc. 9365, 114-123 (2015)
153.
Ceruto, T.* et al.: Mining medical data to obtain fuzzy predicates. Lect. Notes Comput. Sc. 8649, 103-117 (2014)
154.
Fröhlich, F. ; Theis, F.J. & Hasenauer, J.: Uncertainty analysis for non-identifiable dynamical systems: Profile likelihoods, bootstrapping and more. Lect. Notes Comput. Sc. 8859, 61-72 (2014)
155.
Fröhlich, F. ; Hross, S. ; Theis, F.J. & Hasenauer, J.: Radial basis function approximations of Bayesian parameter posterior densities for uncertainty analysis. Lect. Notes Comput. Sc. 8859, 73-85 (2014)
156.
Goebl, S.* ; Meyer-Baese, A.C.* ; Lobbes, M.B.I.* & Plant, C.: Segmentation and kinetic analysis of breast lesions in DCE-MR imaging using ICA. Lect. Notes Comput. Sc. 8649, 45-59 (2014)
157.
Hennersperger, C.* ; Mateus, D. ; Baust, M.* & Navab, N.A.*: A quadratic energy minimization framework for signal loss estimation from arbitrarily sampled ultrasound data. Lect. Notes Comput. Sc. 8674, 373-380 (2014)
158.
Khakhutskyy, V.* et al.: Centroid clustering of cellular lineage trees. Lect. Notes Comput. Sc. 8649, 15-29 (2014)
159.
Peng, T.* et al.: Shading correction for whole slide image using low rank and sparse decomposition. Lect. Notes Comput. Sc. 8673, 33-40 (2014)
160.
Chatelain, P.* et al.: Learning from multiple experts with random forests: Application to the segmentation of the midbrain in 3D ultrasound. Lect. Notes Comput. Sc. 8150, 230-237 (2013)
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