  Glioblastoma is profoundly heterogeneous in regional microstructure and
vasculature. Characterizing the spatial heterogeneity of glioblastoma could
lead to more precise treatment. With unsupervised learning techniques,
glioblastoma MRI-derived radiomic features have been widely utilized for tumor
sub-region segmentation and survival prediction. However, the reliability of
algorithm outcomes is often challenged by both ambiguous intermediate process
and instability introduced by the randomness of clustering algorithms,
especially for data from heterogeneous patients.
  In this paper, we propose an adaptive unsupervised learning approach for
efficient MRI intra-tumor partitioning and glioblastoma survival prediction. A
novel and problem-specific Feature-enhanced Auto-Encoder (FAE) is developed to
enhance the representation of pairwise clinical modalities and therefore
improve clustering stability of unsupervised learning algorithms such as
K-means. Moreover, the entire process is modelled by the Bayesian optimization
(BO) technique with a custom loss function that the hyper-parameters can be
adaptively optimized in a reasonably few steps. The results demonstrate that
the proposed approach can produce robust and clinically relevant MRI
sub-regions and statistically significant survival predictions.
