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.

翻译：与现有全监督方法不同，我们从分布外视角出发，采用一种简单而有效的自监督学习方法重新审视结直肠息肉分割。我们利用掩码自编码器——一种基于重构任务训练的自监督视觉Transformer——学习分布内表示，即健康结肠图像的分布。随后，我们执行分布外重构与推理，通过特征空间标准化将多样化异常样本的潜在分布与健康样本的统计特征对齐。通过计算输入图像与重构图像之间的差异，为每张图像生成逐像素异常分数，并利用该信号进行分布外（即息肉）分割。在六个基准数据集上的实验结果表明，我们的模型具有卓越的分割性能，并能在不同数据集间泛化。我们的代码已公开于 https://github.com/GewelsJI/Polyp-OOD。