Rethinking Polyp Segmentation From An Out-of-distribution Perspective

Document Type

Article

Publication Title

Machine Intelligence Research

Abstract

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 https://github.com/GewelsJI/Polyp-OOD .

DOI

10.1007/s11633-023-1472-2

Publication Date

1-1-2024

Keywords

abdomen, anomaly segmentation, masked autoencoder, out-of-distribution segmentation, Polyp segmentation

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