This study explores an automatic diagnosis method to predict unnecessary nodule biopsy from a small, unbalanced, and pathologically proven database. The automatic diagnosis method is based on a convolutional neural network (CNN) model. Because of the small and unbalanced samples, the presented method aims to improve the transfer learning capability via the VGG16 architecture and optimize the related transfer learning parameters. For comparison purpose, a traditional machine learning method is implemented, which extracts the texture features and classifies the features by support vector machine (SVM). The database includes 68 biopsied nodules, 16 are pathologically proven benign and the remaining 52 are malignant. To consider the volumetric data by the CNN model, each image slice from each nodule volume is selected randomly until all image slices of each nodule are utilized. The leave-one-out and 10-folder cross validations are applied to train and test the randomly selected 68 image slices (one image slice from one nodule) in each experiment, respectively. The averages over all the experimental outcomes are the final results. The experiments revealed that the features from both the medical and the natural images share the similarity of focusing on simpler and less-abstract objects, leading to the conclusion that not the more the transfer convolutional layers, the better the classification results. Transfer learning from other larger datasets can supply additional information to small and unbalanced datasets to improve the classification performance. The presented method has shown the potential to adapt CNN architecture to improve the prediction of unnecessary nodule biopsy from small, unbalanced, and pathologically proven volumetric dataset.

中文翻译：

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通过转移学习从一个小的、不平衡的和病理学证明的数据集中预测不必要的结节活检。

本研究探索了一种自动诊断方法，可从一个小型、不平衡且经病理证实的数据库中预测不必要的结节活检。自动诊断方法基于卷积神经网络（CNN）模型。由于样本小且不平衡，本文提出的方法旨在通过 VGG16 架构提高迁移学习能力并优化相关迁移学习参数。为了比较，实现了传统的机器学习方法，提取纹理特征并通过支持向量机（SVM）对特征进行分类。该数据库包括 68 个活检结节，16 个经病理证实为良性，其余 52 个为恶性。考虑 CNN 模型的体积数据，每个结节体积的每个图像切片都是随机选择的，直到每个结节的所有图像切片都被利用。留一法和 10 折交叉验证分别用于训练和测试每个实验中随机选择的 68 个图像切片（来自一个结节的一个图像切片）。所有实验结果的平均值是最终结果。实验表明，医学图像和自然图像的特征都具有关注更简单和抽象程度较低的对象的相似性，从而得出结论，并不是迁移卷积层越多，分类结果就越好。从其他较大的数据集迁移学习可以为小型和不平衡的数据集提供额外的信息，以提高分类性能。