Rethinking Polyp Segmentation from an Out-of-Distribution Perspective
Ge-Peng Ji Jing Zhang Dylan Campbell Huan Xiong Nick Barnes
Unlike existing fully-supervised approaches, we rethink colorectal polyp
segmentation from an out-of-distribution perspective with a simple but
effective self-supervised learning approach. We leverage the ability of masked
autoencoders -- self-supervised vision transformers trained on a reconstruction
task -- to learn in-distribution representations; here, the distribution of
healthy colon images. We then perform out-of-distribution reconstruction and
inference, with feature space standardisation to align the latent distribution
of the diverse abnormal samples with the statistics of the healthy samples. We
generate per-pixel anomaly scores for each image by calculating the difference
between the input and reconstructed images and use this signal for
out-of-distribution (ie, polyp) segmentation. Experimental results on six
benchmarks show that our model has excellent segmentation performance and
generalises across datasets. Our code is publicly available at
https://github.com/GewelsJI/Polyp-OOD.