T-Net: A Resource-Constrained Tiny Convolutional Neural Network for Medical Image Segmentation

In this paper, we present T-Net, a fully convolutional net-work particularly well suited for resource constrained andmobile devices, which cannot cater for the computationalresources often required by much larger networks. T-NET's design allows for dual-stream information flow both insideas well as outside of the encoder-decoder pair. Here, weuse group convolutions to increase the width of the networkand, in doing so, learn a larger number of low and inter-mediate level features. We have also employed skip connec-tions in order to keep spatial information loss to a minimum.T-Net uses a dice loss for pixel-wise classification which al-leviates the effect of class imbalance. We have performedexperiments with three different applications, retinal vesselsegmentation, skin lesion segmentation and digestive tractpolyp segmentation. In our experiments, T-Net is quite com-petitive, outperforming alternatives with two or even threeorders of magnitude more trainable parameters.