标题： 面向组织病理学图像中浸润性导管癌检测的人机协作系统 显示英文标题

标题： Towards Human-AI Collaboration System for the Detection of Invasive Ductal Carcinoma in Histopathology Images

摘要： 浸润性导管癌（IDC）是乳腺癌最常见的一种形式，早期准确的诊断对于通过指导治疗决策提高患者生存率至关重要。 将医学专业知识与人工智能（AI）相结合，在提高IDC检测的精确性和效率方面具有重大潜力。 在本工作中，我们提出一个人工介入的深度学习系统（HITL），旨在检测组织病理学图像中的IDC。 该系统从由高性能EfficientNetV2S模型提供的初始诊断开始，向人类专家提供AI的反馈。 医疗专业人员随后审查AI生成的结果，纠正任何分类错误的图像，并将修订后的标签整合到训练数据集中，形成从人类返回到AI的反馈循环。 这一迭代过程随着时间的推移改进模型的性能。 EfficientNetV2S模型本身相比文献中现有方法表现出最先进的性能，总体准确率为93.65%。 将人工介入系统纳入其中，通过四个包含分类错误图像的实验组进一步提高了模型的准确性。 这些结果展示了这种协作方法在增强诊断系统中AI性能方面的潜力。 本研究通过人机协作推动了自动化、高效且高度准确的IDC检测方法的发展，为未来AI辅助的医学诊断提供了有前景的方向。 显示英文摘要

摘要： Invasive ductal carcinoma (IDC) is the most prevalent form of breast cancer, and early, accurate diagnosis is critical to improving patient survival rates by guiding treatment decisions. Combining medical expertise with artificial intelligence (AI) holds significant promise for enhancing the precision and efficiency of IDC detection. In this work, we propose a human-in-the-loop (HITL) deep learning system designed to detect IDC in histopathology images. The system begins with an initial diagnosis provided by a high-performance EfficientNetV2S model, offering feedback from AI to the human expert. Medical professionals then review the AI-generated results, correct any misclassified images, and integrate the revised labels into the training dataset, forming a feedback loop from the human back to the AI. This iterative process refines the model's performance over time. The EfficientNetV2S model itself achieves state-of-the-art performance compared to existing methods in the literature, with an overall accuracy of 93.65\%. Incorporating the human-in-the-loop system further improves the model's accuracy using four experimental groups with misclassified images. These results demonstrate the potential of this collaborative approach to enhance AI performance in diagnostic systems. This work contributes to advancing automated, efficient, and highly accurate methods for IDC detection through human-AI collaboration, offering a promising direction for future AI-assisted medical diagnostics.