  Learning High-Resolution representations is essential for semantic
segmentation. Convolutional neural network (CNN)architectures with downstream
and upstream propagation flow are popular for segmentation in medical
diagnosis. However, due to performing spatial downsampling and upsampling in
multiple stages, information loss is inexorable. On the contrary, connecting
layers densely on high spatial resolution is computationally expensive. In this
work, we devise a Loose Dense Connection Strategy to connect neurons in
subsequent layers with reduced parameters. On top of that, using a m-way Tree
structure for feature propagation we propose Receptive Field Chain Network
(RFC-Net) that learns high resolution global features on a compressed
computational space. Our experiments demonstrates that RFC-Net achieves
state-of-the-art performance on Kvasir and CVC-ClinicDB benchmarks for Polyp
segmentation.
