从像素到切片图像：基于偏振模态的病理诊断表示学习
From Pixel to Slide image: Polarization Modality-based Pathological Diagnosis Using Representation Learning
AI总结：
本研究提出一种三阶段表示学习模型，整合像素级与切片级标注区分甲状腺肿瘤，通过偏振特征实现未染色切片的高精度间接诊断。
摘要：
甲状腺癌是最常见的内分泌恶性肿瘤，在临床实践中准确区分良性和恶性甲状腺肿瘤对于制定有效的治疗方案至关重要。在病理学上，由于样本采样不当，甲状腺肿瘤的诊断面临挑战。在本研究中，我们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。该结构包括一种病理结构识别方法（用于预测与甲状腺肿瘤相关的结构），一个编码器-解码器网络（通过学习图像块的特征表示来提取像素级标注信息），以及一个基于注意力的学习机制（用于最终的分类任务）。该机制学习病理区域中不同图像块的重要性，全局考虑每个块的信息。在第三阶段，使用注意力机制聚合区域内图像块的所有信息，随后进行分类以确定该区域的类别。实验结果表明，我们提出的方法能更准确地预测微观结构。经过颜色编码后，该方法在未染色的病理切片上取得了近似苏木精和伊红染色质量的结果，减少了对染色病理切片的需求。此外，通过利用间接测量的概念并从与病变相关的结构中提取偏振特征，所提方法还能对因采样无法获取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。
英文摘要：
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.
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。
