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.

通过图像和结构表示学习，该研究设计了一种三阶段模型来准确区分甲状腺肿瘤，通过颜色编码实现了与血红蛋白和伊红染色相近的效果，同时提供了甲状腺肿瘤的间接诊断技术。

从像素到幻灯片图像：基于表示学习的极化模态病理诊断

From Pixel to Slide image: Polarization Modality-based Pathological  Diagnosis Using Representation Learning

With the rapid expansion of machine learning and deep learning (DL),
researchers are increasingly employing learning-based algorithms to alleviate
diagnostic challenges across diverse medical tasks and applications. While
advancements in diagnostic precision are notable, some researchers have
identified a concerning trend: their models exhibit biased performance across
subgroups characterized by different sensitive attributes. This bias not only
infringes upon the rights of patients but also has the potential to lead to
life-altering consequences. In this paper, we inspect a series of DL
segmentation models using two ultrasound datasets, aiming to assess the
presence of model unfairness in these specific tasks. Our findings reveal that
even state-of-the-art DL algorithms demonstrate unfair behavior in ultrasound
segmentation tasks. These results serve as a crucial warning, underscoring the
necessity for careful model evaluation before their deployment in real-world
scenarios. Such assessments are imperative to ensure ethical considerations and
mitigate the risk of adverse impacts on patient outcomes.

机器学习和深度学习在医学任务和应用中得到广泛应用，然而，研究人员发现模型在不同敏感属性的子组中表现出偏见。本文通过检测一系列深度学习分割模型在超声波分割任务中的表现来评估模型的不公平性，结果显示即使最先进的深度学习算法也存在不公平行为。这些发现强调了在模型部署到实际场景之前对其进行仔细评估的必要性，以确保道德考量并减轻对患者结果的不利影响的风险。