用深度学习与可解释AI提升卵巢癌的自动检测准确率
Automated Detection of Malignant Lesions in the Ovary Using Deep Learning Models and XAI
- 基于InceptionV3等CNN模型构建15种变体，优化卵巢癌识别
- 在增强数据集上达到94%平均准确率，性能最优
- 结合LIME、SHAP等XAI工具解析模型决策，提升临床可信度
恶性细胞不受控制地增殖即为癌症。近年来，医疗从业者通过深度学习模型分析医学数据，在临床决策、疾病诊断和药物发现方面获得更强能力。多数癌症已应用此类技术，但卵巢癌仍面临非侵入性检测不准确、确诊需耗时且侵入性操作的困境。本研究采用LeNet-5、ResNet、VGGNet及GoogLeNet/Inception等卷积神经网络构建15种模型变体，以识别卵巢癌。训练使用Mendeley提供的OvarianCancer&SubtypesDatasetHistopathology数据集。选定最佳模型后，利用LIME、集成梯度和SHAP等可解释AI（XAI）方法解析其黑箱输出。评估指标包括准确率、精确率、召回率、F1分数、ROC曲线和AUC。结果显示，带有ReLU激活函数的紧凑型InceptionV3模型在增强数据集上实现94%的平均性能得分。最后对三种XAI方法进行综合比较，旨在为卵巢癌检测提供更优解决方案。
原文摘要 · Abstract (English)
The unrestrained proliferation of cells that are malignant in nature is cancer. In recent times, medical professionals are constantly acquiring enhanced diagnostic and treatment abilities by implementing deep learning models to analyze medical data for better clinical decision, disease diagnosis and drug discovery. A majority of cancers are studied and treated by incorporating these technologies. However, ovarian cancer remains a dilemma as it has inaccurate non-invasive detection procedures and a time consuming, invasive procedure for accurate detection. Thus, in this research, several Convolutional Neural Networks such as LeNet-5, ResNet, VGGNet and GoogLeNet/Inception have been utilized to develop 15 variants and choose a model that accurately detects and identifies ovarian cancer. For effective model training, the dataset OvarianCancer&SubtypesDatasetHistopathology from Mendeley has been used. After constructing a model, we utilized Explainable Artificial Intelligence (XAI) models such as LIME, Integrated Gradients and SHAP to explain the black box outcome of the selected model. For evaluating the performance of the model, Accuracy, Precision, Recall, F1-Score, ROC Curve and AUC have been used. From the evaluation, it was seen that the slightly compact InceptionV3 model with ReLu had the overall best result achieving an average score of 94% across all the performance metrics in the augmented dataset. Lastly for XAI, the three aforementioned XAI have been used for an overall comparative analysis. It is the aim of this research that the contributions of the study will help in achieving a better detection method for ovarian cancer.
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