揭示卷积神经网络在癌症病理图像分析中的偏见与可靠性问题
Unmasking Biases and Reliability Concerns in Convolutional Neural Networks Analysis of Cancer Pathology Images
AI总结：
研究通过分析13个常用癌症数据集，发现CNN在无临床信息的裁剪图像上仍能保持高准确率，揭示了评估方法可能存在的偏见问题。
摘要：
卷积神经网络在从放射影像中识别不同癌症类型方面显示出良好的有效性。然而，CNN的不透明性使得难以完全理解其工作原理，限制了其评估仅限于实证评估。本文研究了CNN在癌症病理学中评估的可靠性，分析了13个常用癌症基准数据集，使用四种常见CNN架构和不同类型的癌症，如黑色素瘤、癌、结直肠癌和肺癌。通过比较每个模型在由原始图像背景裁剪段组成的无临床相关信息数据集上的准确性，发现即使在无生物医学信息的情况下，CNN模型仍能提供高达93%的准确率。这些结果表明，某些CNN架构对偏见更为敏感。分析显示，机器学习评估的常见实践在应用于癌症病理学时可能导致不可靠的结果。这些偏见非常难以识别，可能误导研究人员在使用可用基准数据集测试CNN方法的有效性时。
英文摘要：
Convolutional Neural Networks have shown promising effectiveness in identifying different types of cancer from radiographs. However, the opaque nature of CNNs makes it difficult to fully understand the way they operate, limiting their assessment to empirical evaluation. Here we study the soundness of the standard practices by which CNNs are evaluated for the purpose of cancer pathology. Thirteen highly used cancer benchmark datasets were analyzed, using four common CNN architectures and different types of cancer, such as melanoma, carcinoma, colorectal cancer, and lung cancer. We compared the accuracy of each model with that of datasets made of cropped segments from the background of the original images that do not contain clinically relevant content. Because the rendered datasets contain no clinical information, the null hypothesis is that the CNNs should provide mere chance-based accuracy when classifying these datasets. The results show that the CNN models provided high accuracy when using the cropped segments, sometimes as high as 93\%, even though they lacked biomedical information. These results show that some CNN architectures are more sensitive to bias than others. The analysis shows that the common practices of machine learning evaluation might lead to unreliable results when applied to cancer pathology. These biases are very difficult to identify, and might mislead researchers as they use available benchmark datasets to test the efficacy of CNN methods.
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