Cai Wentian, Li Yijiang, Chen Yandan, Lin Jing, Huang Zihao, Gao Ping, Gadekallu Thippa Reddy, Wang Wei, Gao Ying
IEEE J Biomed Health Inform. 2024 Sep 5;PP. doi: 10.1109/JBHI.2024.3450013.
Histopathological whole-slide image (WSI) segmentation is essential for precise tissue characterization in medical diagnostics. However, traditional approaches require labor-intensive pixel-level annotations. To this end, we study weakly supervised semantic segmentation (WSSS) which uses patch-level classification labels, reducing annotation efforts significantly. However, the complexity of WSIs and the challenge of sparse classification labels hinder effective dense pixel predictions. Moreover, due to the multi-label nature of WSI, existingapproachesofsingle-labelcontrastivelearningdesignedfortherepresentationofsingle-category, neglecting the presence of other relevant categories and thus fail to adapt to WSI tasks. This paper presents a novel multilabel contrastive learning method for WSSS by incorporating class-specific embedding extraction with LLM features guidance. Specifically, we propose to obtain class-specific embeddings by utilizing classifier weights, followed by a dot-product-based attention fusion method that leverages LLM features to enrich their semantics, facilitating contrastive learning between different classes from single image. Besides, we propose a Robust Learning approach that leverages multi-layer features to evaluate the uncertainty of pseudo-labels, thereby mitigating the impact of noisy pseudo-labels on the learning process of segmentation. Extensive experiments have been conducted on two Histopathological image segmentation datasets, i.e. LUAD dataset and BCSS dataset, demonstrating the effectiveness of our methods with leading performance.
组织病理学全切片图像（WSI）分割对于医学诊断中精确的组织特征描述至关重要。然而，传统方法需要大量人工进行像素级标注。为此，我们研究了弱监督语义分割（WSSS），它使用补丁级分类标签，显著减少了标注工作量。然而，WSI的复杂性以及稀疏分类标签的挑战阻碍了有效的密集像素预测。此外，由于WSI的多标签性质，现有的用于单类别表示的单标签对比学习方法忽略了其他相关类别的存在，因此无法适应WSI任务。本文提出了一种用于WSSS的新颖多标签对比学习方法，通过将特定类别的嵌入提取与大语言模型（LLM）特征指导相结合。具体而言，我们建议利用分类器权重来获得特定类别的嵌入，随后采用基于点积的注意力融合方法，该方法利用LLM特征来丰富其语义，促进从单个图像中不同类别之间的对比学习。此外，我们提出了一种稳健学习方法，该方法利用多层特征来评估伪标签的不确定性，从而减轻有噪声的伪标签对分割学习过程的影响。我们在两个组织病理学图像分割数据集，即LUAD数据集和BCSS数据集上进行了广泛的实验，证明了我们方法的有效性，性能领先。
