Improving the accuracy of medical image interpretation can improve the diagnosis of numerous diseases. We compared different approaches to aggregating repeated decisions about medical images to improve the accuracy of a single decision maker. We tested our algorithms on data from both novices (undergraduates) and experts (medical professionals). Participants viewed images of white blood cells and made decisions about whether the cells were cancerous or not. Each image was shown twice to the participants and their corresponding confidence judgments were collected. The maximum confidence slating (MCS) algorithm leverages metacognitive abilities to consider the more confident response in the pair of responses as the more accurate “final response” (Koriat, 2012), and it has previously been shown to improve accuracy on our task for both novices and experts (Hasan et al., 2021). We compared MCS to similarity-based aggregation (SBA) algorithms where the responses made by the same participant on similar images are pooled together to generate the “final response.” We determined similarity by using two different neural networks where one of the networks had been trained on white blood cells and the other had not. We show that SBA improves performance for novices even when the neural network had no specific training on white blood cell images. Using an informative representation (i.e., network trained on white blood cells) allowed one to aggregate over more neighbors and further boosted the performance of novices. However, SBA failed to improve the performance for experts even with the informative representation. This difference in efficacy of the SBA suggests different decision mechanisms for novices and experts.

中文翻译：

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利用元认知过程和表征相似性改进医学图像决策

提高医学图像判读的准确性可以改善多种疾病的诊断。我们比较了聚合有关医学图像的重复决策的不同方法，以提高单个决策者的准确性。我们根据新手（本科生）和专家（医学专家）的数据测试了我们的算法。参与者查看白细胞的图像并决定这些细胞是否癌变。每个图像向参与者显示两次，并收集他们相应的置信度判断。最大置信度排序（MCS）算法利用元认知能力将响应对中更自信的响应视为更准确的“最终响应”（Koriat，2012），并且之前已证明它可以提高新手和专家任务的准确性（Hasan 等人，2021 年）。我们将 MCS 与基于相似性的聚合 (SBA) 算法进行了比较，在该算法中，同一参与者对相似图像的响应汇集在一起​​，以生成“最终响应”。我们通过使用两个不同的神经网络来确定相似性，其中一个网络已经过白细胞训练，另一个没有。我们表明，即使神经网络没有对白细胞图像进行特定训练，SBA 也可以提高新手的性能。使用信息丰富的表示（即，在白细胞上训练的网络）可以让一个人聚集更多的邻居，并进一步提高新手的表现。然而，即使有信息表示，SBA 也未能提高专家的表现。