首页|Rethinking Polyp Segmentation from An Out-of-distribution Perspective
Rethinking Polyp Segmentation from An Out-of-distribution Perspective
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NETL
NSTL
万方数据
Unlike existing fully-supervised approaches,we rethink colorectal polyp segmentation from an out-of-distribution perspect-ive 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 calcu-lating 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.
NETL
NSTL
万方数据
