Background/Objectives: The major question that confronts a pathologist when evaluating a lymph node biopsy is whether the process is benign or malignant, and the differential diagnosis between follicular lymphoma and reactive lymphoid tissue can be challenging.Methods: This study designed a convolutional neural network based on ResNet architecture to classify a large series of 221 cases, including 177 follicular lymphoma and 44 reactive lymphoid tissue/lymphoid hyperplasia, which were stained with hematoxylin and eosin (H&E). Explainable artificial intelligence (XAI) methods were used for interpretability.Results: The series included 1,004,509 follicular lymphoma and 490,506 reactive lymphoid tissue image-patches at 224 × 244 × 3, and was partitioned into training (70%), validation (10%), and testing (20%) sets. The performance of the training (training and validation sets) had an accuracy of 99.81%. In the testing set, the performance metrics achieved an accuracy of 99.80% at the image-patch level for follicular lymphoma. The other performance parameters were precision (99.8%), recall (99.8%), false positive rate (0.35%), specificity (99.7%), and F1 score (99.9%). Interpretability was analyzed using three methods: grad-CAM, image LIME, and occlusion sensitivity. Additionally, hybrid partitioning was performed to avoid information leakage using a patient-level independent validation set that confirmed high classification performance.Conclusions: Narrow artificial intelligence (AI) can perform differential diagnosis between follicular lymphoma and reactive lymphoma tissue, but it is task-specific and operates within limited constraints. The trained ResNet convolutional neural network (CNN) may be used as transfer learning for larger series of cases and lymphoma diagnoses in the future.
背景/目的：病理学家在评估淋巴结活检时面临的主要问题是判断病变性质为良性或恶性，其中滤泡性淋巴瘤与反应性淋巴组织的鉴别诊断尤为困难。方法：本研究基于ResNet架构设计卷积神经网络，对221例苏木精-伊红（H&E）染色样本（包括177例滤泡性淋巴瘤和44例反应性淋巴组织/淋巴增生）进行大规模分类，并采用可解释人工智能（XAI）方法增强模型可解释性。结果：数据集包含1,004,509个滤泡性淋巴瘤图像块和490,506个反应性淋巴组织图像块（尺寸224×244×3），按70%训练集、10%验证集和20%测试集划分。训练阶段（含训练集与验证集）准确率达99.81%。测试集中，滤泡性淋巴瘤在图像块级别的分类准确率为99.80%，其他性能指标包括精确度（99.8%）、召回率（99.8%）、假阳性率（0.35%）、特异性（99.7%）和F1分数（99.9%）。通过梯度加权类激活映射、局部可解释模型无关解释及遮挡敏感性分析三种方法实现模型可解释性。此外，采用患者级别的独立验证集进行混合分区以避免信息泄露，结果证实了模型的高分类性能。结论：狭义人工智能能够实现滤泡性淋巴瘤与反应性淋巴组织的鉴别诊断，但其具有任务特异性且受限于特定条件。训练完成的ResNet卷积神经网络可作为迁移学习模型，未来有望应用于更大规模的病例分析和淋巴瘤诊断。
肿瘤(癌症)患者之家