Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP

Retinoblastoma is a rare and aggressive form of childhood eye cancer that requires prompt diagnosis and treatment to prevent vision loss and even death. Deep learning models have shown promising results in detecting retinoblastoma from fundus images, but their decision-making process is often considered a "black box" that lacks transparency and interpretability. In this project, we explore the use of LIME and SHAP, two popular explainable AI techniques, to generate local and global explanations for a deep learning model based on InceptionV3 architecture trained on retinoblastoma and non-retinoblastoma fundus images. We collected and labeled a dataset of 400 retinoblastoma and 400 non-retinoblastoma images, split it into training, validation, and test sets, and trained the model using transfer learning from the pre-trained InceptionV3 model. We then applied LIME and SHAP to generate explanations for the model's predictions on the validation and test sets. Our results demonstrate that LIME and SHAP can effectively identify the regions and features in the input images that contribute the most to the model's predictions, providing valuable insights into the decision-making process of the deep learning model. In addition, the use of InceptionV3 architecture with spatial attention mechanism achieved high accuracy of 97\% on the test set, indicating the potential of combining deep learning and explainable AI for improving retinoblastoma diagnosis and treatment.视网膜母细胞瘤（Retinoblastoma）是一种罕见且极具侵袭性的儿童眼部恶性肿瘤，需及时诊断与治疗以避免视力丧失甚至死亡。深度学习模型在基于眼底图像检测视网膜母细胞瘤方面已展现出良好应用前景，但其决策过程常被视为缺乏透明度与可解释性的“黑箱”。本研究探讨了局部可解释模型无关解释（LIME）与夏普利可加解释（SHAP）这两种主流可解释AI技术的应用，用于为基于InceptionV3架构的深度学习模型生成局部与全局解释；该模型以视网膜母细胞瘤与非视网膜母细胞瘤眼底图像为训练数据。本研究收集并标注了包含400张视网膜母细胞瘤图像与400张非视网膜母细胞瘤图像的数据集，将其划分为训练集、验证集与测试集，并基于预训练的InceptionV3模型通过迁移学习完成模型训练。随后，我们应用LIME与SHAP为模型在验证集与测试集上的预测结果生成解释。实验结果表明，LIME与SHAP可有效识别输入图像中对模型预测贡献度最高的区域与特征，为深入理解深度学习模型的决策过程提供了极具价值的视角。此外，搭载空间注意力机制的InceptionV3架构在测试集上实现了97%的高精度，这表明将深度学习与可解释AI相结合，有望助力视网膜母细胞瘤的诊断与治疗水平提升。