  Automatic polyp segmentation has proven to be immensely helpful for endoscopy
procedures, reducing the missing rate of adenoma detection for endoscopists
while increasing efficiency. However, classifying a polyp as being neoplasm or
not and segmenting it at the pixel level is still a challenging task for
doctors to perform in a limited time. In this work, we propose a fine-grained
formulation for the polyp segmentation problem. Our formulation aims to not
only segment polyp regions, but also identify those at high risk of malignancy
with high accuracy. In addition, we present a UNet-based neural network
architecture called NeoUNet, along with a hybrid loss function to solve this
problem. Experiments show highly competitive results for NeoUNet on our
benchmark dataset compared to existing polyp segmentation models.
