  Overparameterized deep learning networks have shown impressive performance in
the area of automatic medical image segmentation. However, they achieve this
performance at an enormous cost in memory, runtime, and energy. A large source
of overparameterization in modern neural networks results from doubling the
number of feature maps with each downsampling layer. This rapid growth in the
number of parameters results in network architectures that require a
significant amount of computing resources, making them less accessible and
difficult to use. By keeping the number of feature maps constant throughout the
network, we derive a new CNN architecture called PocketNet that achieves
comparable segmentation results to conventional CNNs while using less than 3%
of the number of parameters.
