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

Tariq Mahmood Khan, Antonio Robles-Kelly, Syed Saud Naqvi

Abstract: In this paper, we present T-Net, a fully convolutional network particularly well suited for resource constrained and mobile devices, which cannot cater for the computational resources often required by much larger networks. T-NET’s design allows for dual-stream information flow both inside as well as outside of the encoder-decoder pair. Here, we use group convolutions to increase the width of the network and, in doing so, learn a larger number of low and intermediate level features. We have also employed skip connections in order to keep spatial information loss to a minimum. T-Net uses a dice loss for pixel-wise classification which alleviates the effect of class imbalance. We have performed experiments with three different applications, retinal vessel segmentation, skin lesion segmentation and digestive tract polyp segmentation. In our experiments, T-Net is quite competitive, outperforming alternatives with two or even three orders of magnitude more trainable parameters.