面向生物医学语义分割的分布外数据监督
Out-of-distribution data supervision towards biomedical semantic segmentation
AI总结：
针对生物医学分割中的前景背景误分类问题，提出数据为中心的框架Med-OoD，通过引入无需外部数据和额外标注的分布外数据监督，无缝提升分割网络性能，并探索了完全使用无前景标签的OoD数据训练网络的新范式。
摘要：
生物医学分割网络在有限且不完美的医学数据集上学习时，很容易遭受前景与背景对象之间的意外误分类。受分布外（OoD）数据在其他视觉任务中强大能力的启发，我们提出了一个以数据为中心的框架Med-OoD，通过将OoD数据监督引入全监督生物医学分割来解决此问题，且无需以下任何条件：外部数据源、特征正则化目标、额外标注。我们的方法可以无缝集成到分割网络中，而无需对架构进行任何修改。大量实验表明，Med-OoD在很大程度上防止了各种分割网络在医学图像上的像素误分类，并在Lizard数据集上取得了显著的性能提升。我们还提出了一种新兴的学习范式，即完全使用缺乏前景类标签的OoD数据来训练医学分割网络，令人惊讶的是，测试结果达到了76.1%的mIoU。我们希望这种学习范式能吸引人们重新思考OoD数据的作用。代码可在https://github.com/StudioYG/Med-OoD获取。
英文摘要：
Biomedical segmentation networks easily suffer from the unexpected misclassification between foreground and background objects when learning on limited and imperfect medical datasets. Inspired by the strong power of Out-of-Distribution (OoD) data on other visual tasks, we propose a data-centric framework, Med-OoD to address this issue by introducing OoD data supervision into fully-supervised biomedical segmentation with none of the following needs: (i) external data sources, (ii) feature regularization objectives, (iii) additional annotations. Our method can be seamlessly integrated into segmentation networks without any modification on the architectures. Extensive experiments show that Med-OoD largely prevents various segmentation networks from the pixel misclassification on medical images and achieves considerable performance improvements on Lizard dataset. We also present an emerging learning paradigm of training a medical segmentation network completely using OoD data devoid of foreground class labels, surprisingly turning out 76.1% mIoU as test result. We hope this learning paradigm will attract people to rethink the roles of OoD data. Code is made available at https://github.com/StudioYG/Med-OoD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
