  Deep learning models for medical image segmentation are primarily
data-driven. Models trained with more data lead to improved performance and
generalizability. However, training is a computationally expensive process
because multiple hyper-parameters need to be tested to find the optimal setting
for best performance. In this work, we focus on accelerating the estimation of
hyper-parameters by proposing two novel methodologies: proxy data and proxy
networks. Both can be useful for estimating hyper-parameters more efficiently.
We test the proposed techniques on CT and MR imaging modalities using
well-known public datasets. In both cases using one dataset for building proxy
data and another data source for external evaluation. For CT, the approach is
tested on spleen segmentation with two datasets. The first dataset is from the
medical segmentation decathlon (MSD), where the proxy data is constructed, the
secondary dataset is utilized as an external validation dataset. Similarly, for
MR, the approach is evaluated on prostate segmentation where the first dataset
is from MSD and the second dataset is PROSTATEx. First, we show higher
correlation to using full data for training when testing on the external
validation set using smaller proxy data than a random selection of the proxy
data. Second, we show that a high correlation exists for proxy networks when
compared with the full network on validation Dice score. Third, we show that
the proposed approach of utilizing a proxy network can speed up an AutoML
framework for hyper-parameter search by 3.3x, and by 4.4x if proxy data and
proxy network are utilized together.
