PocketNet: A Smaller Neural Network for 3D Medical Image Segmentation
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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.
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