  Detection of colon polyps has become a trending topic in the intersecting
fields of machine learning and gastrointestinal endoscopy. The focus has mainly
been on per-frame classification. More recently, polyp segmentation has gained
attention in the medical community. Segmentation has the advantage of being
more accurate than per-frame classification or object detection as it can show
the affected area in greater detail. For our contribution to the EndoCV 2021
segmentation challenge, we propose two separate approaches. First, a
segmentation model named TriUNet composed of three separate UNet models.
Second, we combine TriUNet with an ensemble of well-known segmentation models,
namely UNet++, FPN, DeepLabv3, and DeepLabv3+, into a model called
DivergentNets to produce more generalizable medical image segmentation masks.
In addition, we propose a modified Dice loss that calculates loss only for a
single class when performing multiclass segmentation, forcing the model to
focus on what is most important. Overall, the proposed methods achieved the
best average scores for each respective round in the challenge, with TriUNet
being the winning model in Round I and DivergentNets being the winning model in
Round II of the segmentation generalization challenge at EndoCV 2021. The
implementation of our approach is made publicly available on GitHub.
