Rethinking Polyp Segmentation from an Out-of-Distribution Perspective
- URL: http://arxiv.org/abs/2306.07792v1
- Date: Tue, 13 Jun 2023 14:13:16 GMT
- Title: Rethinking Polyp Segmentation from an Out-of-Distribution Perspective
- Authors: Ge-Peng Ji, Jing Zhang, Dylan Campbell, Huan Xiong, Nick Barnes
- Abstract summary: We leverage the ability of masked autoencoders -- self-supervised vision transformers trained on a reconstruction task -- to learn in-distribution representations.
We 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.
Experimental results on six benchmarks show that our model has excellent segmentation performance and generalises across datasets.
- Score: 37.1338930936671
- License: http://creativecommons.org/licenses/by/4.0/
- 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 (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.
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