可解释人工智能方法在医学图像分类上的分析
Analysis of Explainable Artificial Intelligence Methods on Medical Image Classification
- Veermata Jijabai Technological Institute（维尔马塔·吉贾巴伊技术学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文评估了三种可解释人工智能方法在两个用于肺癌组织病理学图像分类的卷积神经网络模型上的表现，通过可视化和性能分析探讨XAI在医疗领域的应用。
AI中文摘要：
深度学习在图像分类等计算机视觉任务中的应用，使得此类系统的性能迅速提升。由于这些系统实用性的大幅提高，人工智能在许多关键任务中的应用激增。在医学领域，医学图像分类系统因其高准确率以及在许多任务中与人类医生几乎持平的表现而正被广泛采用。然而，这些人工智能系统极其复杂，由于难以解释究竟是什么导致了这些模型做出的预测，科学家将其视为黑盒。当这些系统被用于辅助高风险决策时，能够理解、验证和证明模型得出的结论是极其重要的。用于洞察黑盒模型的研究技术属于可解释人工智能（XAI）领域。在本文中，我们评估了三种不同的XAI方法，应用于两个被训练用于从组织病理学图像中分类肺癌的卷积神经网络模型。我们可视化了输出并分析了这些方法的性能，以便更好地理解如何在医疗领域应用可解释人工智能。
英文摘要：
The use of deep learning in computer vision tasks such as image classification has led to a rapid increase in the performance of such systems. Due to this substantial increment in the utility of these systems, the use of artificial intelligence in many critical tasks has exploded. In the medical domain, medical image classification systems are being adopted due to their high accuracy and near parity with human physicians in many tasks. However, these artificial intelligence systems are extremely complex and are considered black boxes by scientists, due to the difficulty in interpreting what exactly led to the predictions made by these models. When these systems are being used to assist high-stakes decision-making, it is extremely important to be able to understand, verify and justify the conclusions reached by the model. The research techniques being used to gain insight into the black-box models are in the field of explainable artificial intelligence (XAI). In this paper, we evaluated three different XAI methods across two convolutional neural network models trained to classify lung cancer from histopathological images. We visualized the outputs and analyzed the performance of these methods, in order to better understand how to apply explainable artificial intelligence in the medical domain.
