肿瘤细胞度（TC）是评估器官肿瘤负荷的重要指标。然而，由于大量的病理图像和病理学家之间的测量结果不一致，手动细胞计数并不可行。 PAIP 2023 挑战赛旨在利用人工智能解决这个问题。这一挑战提出了两个主要障碍：需要评估胰腺训练数据以在结肠中有效使用，以及分割结果中聚集细胞的常见错误计数。为了解决这些问题，我们提出了一种新颖的管道。它包括通道归一化，可标准化 RGB 值，以确保不同器官的模型性能一致。通过引入CacoX（一种用于精确细胞分割的专用模型），我们使用协调注意门来进行精确的细胞定位和非局部学习。最后，分水岭算法的实施允许自动分离簇状细胞。这种方法在 PAIP 2023 挑战赛中以令人印象深刻的 ICC 分数 95.69% 获得第三名。

Tumor Cellularity (TC) is an important metric for assessing organ tumor burden. However, manual cell counting is not feasible due to large volumes of pathology images and inconsistent measurements between pathologists. The PAIP 2023 Challenge aimed to solve this problem using AI. The challenge presented two main obstacles: the need to evaluate pancreas-trained data for effective use in the colon, and the common miscounting of clustered cells in segmentation results. To address these, we proposed a novel pipeline. It included channel normalization, which standardizes RGB values to ensure consistent model performance across different organs. By introducing CacoX, a specialized model for accurate cell segmentation, we used Coordinate Attention Gates for accurate cell localization and non-local learning. Finally, the implementation of a watershed algorithm allowed the automatic separation of clustered cells. This approach secured 3rd place in the PAIP 2023 Challenge with an impressive ICC score of 95.69%.