  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.
