To improve the recognition ability of computer-aided breast mass
classification among mammographic images, in this work we explore the
state-of-the-art classification networks to develop an ensemble mechanism.
First, the regions of interest (ROIs) are obtained from the original dataset,
and then three models, i.e., XceptionNet, DenseNet, and EfficientNet, are
trained individually. After training, we ensemble the mechanism by summing the
probabilities outputted from each network which enhances the performance up to
5%. The scheme has been validated on a public dataset and we achieved accuracy,
precision, and recall 88%, 85%, and 76% respectively.

本研究探索了三种分类网络，即 XceptionNet、DenseNet 和 EfficientNet，通过集成机制来提高计算机辅助乳腺肿块分类的识别能力。在公共数据集上验证该方案，可以获得 88％的准确度，85％的精度和 76％的召回率。

乳腺肿瘤分类的集成卷积神经网络

Ensemble CNNs for Breast Tumor Classification

This study aims to develop a novel computer-aided diagnosis (CAD) scheme for
mammographic breast mass classification using semi-supervised learning.
Although supervised deep learning has achieved huge success across various
medical image analysis tasks, its success relies on large amounts of
high-quality annotations, which can be challenging to acquire in practice. To
overcome this limitation, we propose employing a semi-supervised method, i.e.,
virtual adversarial training (VAT), to leverage and learn useful information
underlying in unlabeled data for better classification of breast masses.
Accordingly, our VAT-based models have two types of losses, namely supervised
and virtual adversarial losses. The former loss acts as in supervised
classification, while the latter loss aims at enhancing model robustness
against virtual adversarial perturbation, thus improving model
generalizability. To evaluate the performance of our VAT-based CAD scheme, we
retrospectively assembled a total of 1024 breast mass images, with equal number
of benign and malignant masses. A large CNN and a small CNN were used in this
investigation, and both were trained with and without the adversarial loss.
When the labeled ratios were 40% and 80%, VAT-based CNNs delivered the highest
classification accuracy of 0.740 and 0.760, respectively. The experimental
results suggest that the VAT-based CAD scheme can effectively utilize
meaningful knowledge from unlabeled data to better classify mammographic breast
mass images.

本研究旨在开发一种新的计算机辅助诊断 (CAD) 方案，以利用半监督学习对乳腺 X-ray 图像进行良恶性分类，采用虚拟对抗培训方法，提高模型的鲁棒性和分类准确性。