Abstract
Polyps are among the earliest sign of Colorectal Cancer, with their detection and segmentation representing a key milestone for automatic colonoscopy analysis. This works describes our solution to the EndoCV 2021 challenge, within the sub-track of polyp segmentation. We build on our recently developed framework of pretrained double encoder-decoder networks, which has achieved state-of-the-art results for this task, but we enhance the training process to account for the high variability and heterogeneity of the data provided in this competition. Specifically, since the available data comes from six different centers, it contains highly variable resolutions and image appearances. Therefore, we introduce a center-sampling training procedure by which the origin of each image is taken into account for deciding which images should be sampled for training. We also increase the representation capability of the encoder in our architecture, in order to provide a more powerful encoding step that can better capture the more complex information present in the data. Experimental results are promising and validate our approach for the segmentation of polyps in a highly heterogeneous data scenarios.
Adrian Galdran was funded by a Marie Skłodowska-Curie Global Fellowship (No 892297).