Invertible Residual Network with Regularization for Effective Medical Image Segmentation

Understand

Deep Convolutional Neural Networks (CNNs) i.e.

• Residual Networks (ResNets) have been used successfully for many computer vision tasks, but are difficult to scale to 3D volumetric medical data.
• Memory is increasingly often the bottleneck when training 3D Convolutional Neural Networks (CNNs).
• Recently, invertible neural networks have been applied to significantly reduce activation memory footprint when training neural networks with backpropagation thanks to the invertible functions that allow retrieving its input from its output without storing intermediate activations in memory to perform the backpropagation.