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收稿：2023-12-11，
修回：2024-03-13，
纸质出版：2024-12-16
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目的
2
病理切片的显微高光谱图像包含生物组织反射的光谱信息，为胆管癌组织细胞的分类诊断提供基础。目前病理高光谱图像分类算法的性能大多依赖于高质量标注数据集，然而数据标注过程耗时、费力。基于自监督的特征提取算法可以缓解数据标注难题。因此，本文提出了自监督提取光谱序列和语义信息的胆管癌显微高光谱图像分类方法，提升自监督方法的特征提取能力及分类精度。
方法
2
首先，从自然语言处理中借鉴了具有序列信息建模能力的Transformer架构，将高光谱图像每个像素反射的光谱曲线看做一个光谱序列，设计Transformer自编码器，通过位置嵌入和注意力模块有效关注光谱序列间的差异，从而更好地学习到光谱序列信息。其次，图像经Transformer编码器结构图像增强得到正样本后，设计卷积自编码器作为另一组图像增强，获取对比学习需要的负样本。随后通过新颖的原型对比学习网络捕获图像中的高级语义信息，网络提取特征的过程使用未标记数据。最后，通过少量标记数据微调下游分类任务网络得到分类结果。
结果
2
在多维胆管癌病理高光谱数据集的8个场景上进行实验，结果表明，与现有7种有监督的特征提取方法和5种无监督的特征提取算法相比，本文方法提取的特征在下游分类任务中能达到更高的分类精度，平均总体分类精度达到96.63%。
结论
2
本文方法能从未标记的胆管癌显微高光谱图像中提取有效特征，特征应用于分类任务中达到较高的分类精度，缓解了病理高光谱图像数据标注难题，对胆管癌的医学诊断具有一定的研究价值和现实意义。
Objective
2
Cholangiocarcinoma is a type of cancer with high fatality rate， and the early detection and treatment of cancer can significantly reduce its incidence. Digital diagnosis of pathological sections can effectively improve the accuracy and efficiency of cancer diagnosis. Microscopic hyperspectral images of pathological sections contain richer spectral information than color images. Due to the specific spectral response of biological tissues， pathological tissues have different spectral characteristics from normal tissues， and meaningful and rich spectral information provides great potential for the classification of cancer cells and healthy cells. The performance of most pathologic hyperspectral image classification algorithms is highly dependent on high-quality labeled datasets， but pathologic hyperspectral images need to be manually labeled by experienced pathologists， which can be time consuming and laborious. The feature extraction algorithm based on self-supervision initially extracts features from unlabeled image data in an unsupervised way by designing pretext tasks and then transfers these image data to downstream tasks. After fine-tuning the downstream task network with a limited number of labeled samples， these algorithms can achieve a supervised learning performance and alleviate the data annotation problem. However， traditional contrast self-supervised learning shows limitations in extracting high-level semantic information， and an image enhancement method specific to pathological hyperspectral images is not yet available. Therefore， this paper proposes a self-supervised method to extract sequential spectral data and semantic information from hyperspectral images of cholangiocarcinoma and improve the feature extraction capability and classification accuracy of the self-supervised method.
Method
2
Hyperspectral images are different from natural images in that image enhancement techniques， such as color transformation， can change spectral information. It is meaningful to use the encoder structure as an image enhancement method for hyperspectral images. However， the encoder used in existing methods is based on the convolutional neural network （CNN）， and the feature map extracted by the CNN corresponds to the local receptive field and ignores the global information of the spectral dimension. Given the limited ability of CNN in characterizing spectral sequence data， this paper first designs a Transformer encoder structure for image enhancement， which retains the details of the sequence in the original image. Borrowing from natural language processing， the Transformer architecture with sequential information modeling capability takes the spectral curve reflected by each pixel of the hyperspectral image as a spectral sequence. Transformer then uses position embedding and attention module to pay attention to the differences among spectral sequences and to efficiently learn spectral sequence information. Second， after the image is enhanced with a Transformer encoder structure to obtain positive samples， the convolutional autoencoder can be used as another set of image enhancement to obtain negative samples required for contrastive learning. To address the problem where traditional contrastive learning extracts features through low-level image differences， thus resulting in its limited ability to extract advanced semantic information， this paper applies prototypical contrastive learning to extract features from pathological hyperspectral images. Positive and negative samples are trained through the clustering and instance discrimination tasks of a prototypical contrastive learning network to learn advanced semantic information in images. The above process of extracting features from network structures uses unlabeled data. Finally， the classification results are obtained by fine-tuning the downstream classification task network with a few labeled features.
Result
2
Experiments were conducted on eight scenes in the hyperspectral dataset of multidimensional cholangiocarcinoma pathology. These scenes were selected from eight patients. To ensure the representativeness of these scenes， different cancer cell morphology， cancer cell proportion， and spectral response curve were used in each scene. The proportion of cancer regions in scenes 2， 3， and 8 only accounted for 1/8 of the whole picture. Experiments were conducted on each scenario， where 5% of the data was labeled for training and 95% was used for testing. To verify the effectiveness of the proposed self-supervised method proposed on pathological hyperspectral datasets， this method was compared with 12 widely used algorithms and networks， including 7 supervised feature extraction methods and 5 unsupervised feature extraction algorithms. Experimental results show that the features extracted by the proposed method achieve optimal results in downstream classification tasks， with an average overall accuracy of 96.63%， average accuracy of reaching 95.37%， and average Kappa coefficient of 0.91. Ablation experiments were also conducted to verify that compared with the convolutional module， the Transformer module pays more attention to sequence details when extracting features after adding the self-attention mechanism and multi-head attention mechanism， which can effectively retain original image information and achieve high classification accuracy. The prototypical contrastive learning module adds a clustering process on the basis of contrastive learning and achieves high classification accuracy， thereby proving that the prototypical contrastive learning module can effectively extract high-level semantic information from microscopic hyperspectral images of cholangiocarcinoma. Results of the dimensionality reduction experiment also show that the semantic features extracted by the proposed method are linearly separable.
Conclusion
2
The proposed method can extract effective features from unlabeled hyperspectral images of cholangiocarcinoma， and these features can be applied to classification tasks to achieve high classification accuracy and alleviate the problem of pathological hyperspectral image data labeling. This method carries certain research value and practical significance for the medical diagnosis of cholangiocarcinoma.
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