EndoNet：用于在组织学切片上自动计算H评分的模型
EndoNet: model for automatic calculation of H-score on histological slides
AI总结：
本研究针对H评分人工计算耗时、精度有限的问题，提出基于神经网络的EndoNet模型，通过细胞核关键点检测与H评分模块实现自动计算，在测试集上达0.77mAP，可适配不同评估习惯，提升病理工作效率。
摘要：
H评分是一种半定量方法，通过结合染色强度与染色细胞核占比，评估组织样本中蛋白质的存在与分布情况。该方法应用广泛，但耗时较长，且准确性和精确性存在局限。计算机辅助方法或可突破这些限制，提升病理科医生的工作流程效率。本研究中，我们开发了EndoNet模型，用于在组织学切片上自动计算H评分。我们提出的方法基于神经网络，包含两个主要部分：第一部分是检测模型，用于预测细胞核中心的关键点；第二部分是H评分模块，利用预测关键点的平均像素值计算H评分数值。我们的模型在1780张尺寸为100×100μm的标注切片块上进行了训练与验证，在测试数据集上取得了0.77的mAP（平均精度均值）。此外，该模型可针对特定专家或整个实验室进行调整，以复现其H评分的计算方式。综上，EndoNet在组织学切片分析中有效且鲁棒，能够优化并显著加快病理科医生的工作。
英文摘要：
H-score is a semi-quantitative method used to assess the presence and distribution of proteins in tissue samples by combining the intensity of staining and percentage of stained nuclei. It is widely used but time-consuming and can be limited in accuracy and precision. Computer-aided methods may help overcome these limitations and improve the efficiency of pathologists' workflows. In this work, we developed a model EndoNet for automatic calculation of H-score on histological slides. Our proposed method uses neural networks and consists of two main parts. The first is a detection model which predicts keypoints of centers of nuclei. The second is a H-score module which calculates the value of the H-score using mean pixel values of predicted keypoints. Our model was trained and validated on 1780 annotated tiles with a shape of 100x100 $μm$ and performed 0.77 mAP on a test dataset. Moreover, the model can be adjusted to a specific specialist or whole laboratory to reproduce the manner of calculating the H-score. Thus, EndoNet is effective and robust in the analysis of histology slides, which can improve and significantly accelerate the work of pathologists.
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