基于语义树的损失函数提升稀疏标注高光谱分割精度
Tree-based Semantic Losses: Application to Sparsely-supervised Large Multi-class Hyperspectral Segmentation
- 构建标签语义树结构，设计两种树形语义损失函数
- 在107类稀疏标注数据上达到当前最优性能
- 可同时检测异常像素且不影响正常分割效果
高光谱成像（HSI）在手术应用中展现出巨大潜力，能提供肉眼无法察觉的生物组织差异信息。为训练视觉系统区分大量细微差异类别，研究正推进精细化标注工作。然而，现有生物医学分割方法对所有错误一视同仁，未能利用标签空间中的类别语义关系。本文提出两种基于树结构的语义损失函数，利用标签的层级组织特性。进一步将这些损失融入一种近期提出的稀疏、无背景标注训练方法。大量实验表明，该方法在包含107个类别的稀疏标注高光谱数据集上达到当前最优表现。此外，该方法可在不损害分布内（ID）像素分割性能的前提下，有效检测分布外（OOD）像素。
原文摘要 · Abstract (English)
Hyperspectral imaging (HSI) shows great promise for surgical applications, offering detailed insights into biological tissue differences beyond what the naked eye can perceive. Refined labelling efforts are underway to train vision systems to distinguish large numbers of subtly varying classes. However, commonly used learning methods for biomedical segmentation tasks penalise all errors equivalently and thus fail to exploit any inter-class semantics in the label space. In this work, we introduce two tree-based semantic loss functions which take advantage of a hierarchical organisation of the labels. We further incorporate our losses in a recently proposed approach for training with sparse, background-free annotations. Extensive experiments demonstrate that our proposed method reaches state-of-the-art performance on a sparsely annotated HSI dataset comprising $107$ classes organised in a clinically-defined semantic tree structure. Furthermore, our method enables effective detection of out-of-distribution (OOD) pixels without compromising segmentation performance on in-distribution (ID) pixels.
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