增强乳腺X光摄影中的乳腺癌诊断：卷积神经网络与可解释AI的评估与集成
Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI
- Salim Habib University（萨利姆哈比布大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出结合卷积神经网络与可解释AI的集成框架，利用CBIS-DDSM数据集和迁移学习增强乳腺癌诊断，并通过Hausdorff测度评估XAI解释与专家标注的一致性，提升AI辅助诊断的可信度与临床集成。
AI中文摘要：
用于从乳腺X光图像中诊断乳腺癌的深度学习（DL）模型通常作为“黑盒”运行，这使得医疗保健专业人员难以信任和理解其决策过程。本研究提出了一个结合卷积神经网络（CNNs）和可解释人工智能（XAI）的集成框架，使用CBIS-DDSM数据集增强乳腺癌的诊断。该方法包含一个精细的数据预处理流水线和先进的数据增强技术，以抵消数据集的局限性，并采用了使用预训练网络（如VGG-16、Inception-V3和ResNet）的迁移学习。我们研究的一个重点是评估XAI在解释模型预测方面的有效性，通过利用Hausdorff测度定量评估AI生成的解释与专家标注之间的一致性来突出这一点。这种方法对于XAI在促进AI辅助诊断中的可信度和伦理公平性至关重要。我们的研究结果说明了CNNs和XAI在推进乳腺癌诊断方法方面的有效协作，从而促进先进AI技术在临床环境中的更无缝集成。通过增强AI驱动决策的可解释性，这项工作为AI系统与医疗从业者之间的改进合作奠定了基础，最终丰富了患者护理。此外，我们研究的影响远超当前方法。它鼓励进一步研究如何结合多模态数据并改进AI解释，以满足临床实践的需求。
英文摘要：
The Deep learning (DL) models for diagnosing breast cancer from mammographic images often operate as "black boxes", making it difficult for healthcare professionals to trust and understand their decision-making processes. The study presents an integrated framework combining Convolutional Neural Networks (CNNs) and Explainable Artificial Intelligence (XAI) for the enhanced diagnosis of breast cancer using the CBIS-DDSM dataset. The methodology encompasses an elaborate data preprocessing pipeline and advanced data augmentation techniques to counteract dataset limitations and transfer learning using pre-trained networks such as VGG-16, Inception-V3 and ResNet was employed. A focal point of our study is the evaluation of XAI's effectiveness in interpreting model predictions, highlighted by utilizing the Hausdorff measure to assess the alignment between AI-generated explanations and expert annotations quantitatively. This approach is critical for XAI in promoting trustworthiness and ethical fairness in AI-assisted diagnostics. The findings from our research illustrate the effective collaboration between CNNs and XAI in advancing diagnostic methods for breast cancer, thereby facilitating a more seamless integration of advanced AI technologies within clinical settings. By enhancing the interpretability of AI driven decisions, this work lays the groundwork for improved collaboration between AI systems and medical practitioners, ultimately enriching patient care. Furthermore, the implications of our research extended well beyond the current methodologies. It encourages further research into how to combine multimodal data and improve AI explanations to meet the needs of clinical practice.
