Rethinking Polyp Segmentation From An Out-of-distribution Perspective

Ge Peng Ji (Gepeng Ji), Jing Zhang*, Dylan Campbell, Huan Xiong, Nick Barnes

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 (i.e., 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 .

Original languageEnglish
JournalMachine Intelligence Research
Issue number4
Publication statusPublished - 2 Jan 2024


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