Thyroid cancer is the most common endocrine malignancy, and accurately distinguishing between benign and malignant thyroid tumors is crucial for developing effective treatment plans in clinical practice. Pathologically, thyroid tumors pose diagnostic challenges due to improper specimen sampling. In this study, we have designed a three-stage model using representation learning to integrate pixel-level and slice-level annotations for distinguishing thyroid tumors. This structure includes a pathology structure recognition method to predict structures related to thyroid tumors, an encoder-decoder network to extract pixel-level annotation information by learning the feature representations of image blocks, and an attention-based learning mechanism for the final classification task. This mechanism learns the importance of different image blocks in a pathological region, globally considering the information from each block. In the third stage, all information from the image blocks in a region is aggregated using attention mechanisms, followed by classification to determine the category of the region. Experimental results demonstrate that our proposed method can predict microscopic structures more accurately. After color-coding, the method achieves results on unstained pathology slides that approximate the quality of Hematoxylin and eosin staining, reducing the need for stained pathology slides. Furthermore, by leveraging the concept of indirect measurement and extracting polarized features from structures correlated with lesions, the proposed method can also classify samples where membrane structures cannot be obtained through sampling, providing a potential objective and highly accurate indirect diagnostic technique for thyroid tumors.

翻译：甲状腺癌是最常见的内分泌恶性肿瘤，在临床实践中准确鉴别甲状腺肿瘤的良恶性对制定有效治疗方案至关重要。病理学上，甲状腺肿瘤因标本取样不当而带来诊断挑战。本研究设计了一个三阶段模型，利用表示学习整合像素级和切片级标注以区分甲状腺肿瘤。该结构包括：预测甲状腺肿瘤相关结构的病理结构识别方法、通过学习图像块特征表示提取像素级标注信息的编码器-解码器网络，以及用于最终分类任务的基于注意力的学习机制。该机制通过全局考虑各图像块信息，学习病理区域中不同图像块的重要性。在第三阶段，采用注意力机制聚合区域内所有图像块信息，随后进行分类以判定区域类别。实验结果表明，所提方法能够更准确地预测微观结构。经伪彩色处理后，该方法在未染色病理切片上达到了近似于苏木精-伊红染色的质量，减少了对染色病理切片的需求。此外，通过利用间接测量概念并从与病灶相关的结构中提取偏振特征，所提方法还可对无法通过取样获得膜结构的样本进行分类，为甲状腺肿瘤提供了一种潜在的客观且高精度的间接诊断技术。