School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, People's Republic of China.
Shandong Provincial Hospital, Jinan, 250013, People's Republic of China.
Int J Comput Assist Radiol Surg. 2022 Apr;17(4):639-648. doi: 10.1007/s11548-022-02565-8. Epub 2022 Feb 12.
Micropapillary adenocarcinoma is a distinctive histological subtype of lung adenocarcinoma with poor prognosis. Computer-aided diagnosis method has the potential to provide help for its early diagnosis. But the implementation of the existing methods largely relies on massive manually labeled data and consumes a lot of time and energy. To tackle these problems, we propose a framework that applies semi-supervised learning method to detect micropapillary adenocarcinoma, which aims to utilize labeled and unlabeled data better.
The framework consists of a teacher model and a student model. The teacher model is first obtained by using the labeled data. Then, it makes predictions on unlabeled data as pseudo-labels for students. Finally, high-quality pseudo-labels are selected and associated with the labeled data to train the student model. During the learning process of the student model, augmentation is added so that the student model generalizes better than the teacher model.
Experiments are conducted on our own whole slide micropapillary lung adenocarcinoma histopathology image dataset and we selected 3527 patches for the experiment. In the supervised learning, our detector achieves a precision of 0.762 and recall of 0.884. In the semi-supervised learning, our method achieves a precision of 0.775 and recall of 0.896; it is superior to other methods.
We proposed a semi-supervised learning framework for micropapillary adenocarcinoma detection, which has better performance in utilizing both labeled and unlabeled data. In addition, the detector we designed improves the detection accuracy and speed and achieves promising results in detecting micropapillary adenocarcinoma.
微乳头腺癌是肺腺癌的一种独特组织学亚型，预后较差。计算机辅助诊断方法有可能为其早期诊断提供帮助。但是，现有方法的实施在很大程度上依赖于大量手动标记的数据，并且需要耗费大量的时间和精力。为了解决这些问题，我们提出了一种应用半监督学习方法来检测微乳头腺癌的框架，旨在更好地利用有标记和无标记的数据。
该框架由教师模型和学生模型组成。首先通过使用标记数据来获得教师模型。然后，它对未标记数据进行预测，作为学生的伪标签。最后，选择高质量的伪标签并将其与标记数据关联，以训练学生模型。在学生模型的学习过程中，添加了增强，以使学生模型比教师模型更好地泛化。
在我们自己的全切片微乳头肺腺癌组织病理学图像数据集上进行了实验，我们为实验选择了 3527 个补丁。在有监督学习中，我们的探测器的精度为 0.762，召回率为 0.884。在半监督学习中，我们的方法的精度为 0.775，召回率为 0.896；它优于其他方法。
我们提出了一种用于微乳头腺癌检测的半监督学习框架，它在利用有标记和无标记数据方面具有更好的性能。此外，我们设计的探测器提高了检测精度和速度，在检测微乳头腺癌方面取得了有希望的结果。
