Background

Over the last years, the most relevant results in the context of polyp detection were achieved through deep learning techniques. However, the most common obstacles in this field are the small datasets with a reduced number of samples and the lack of data variability. This paper describes a method to reduce this limitation and improve polyp detection results using publicly available colonoscopic datasets.

Methods

To address this issue, we increased the number and variety of images from the original dataset. Our method consists on adding polyps to the dataset images. The developed algorithm performs a rigorous selection of the best region within the image to receive the polyp. This procedure preserves the realistic features of the images while creating more diverse samples for training purposes. Our method allows copying existing polyps to new non-polypoid target regions. We also develop a strategy to generate new and more varied polyps through generative adversarial neural networks. Hence, the developed approach enriches the training data, creating automatically new samples with their appropriate labels.

Results

We applied the proposed data enhancement over a colonic polyp dataset. Thus, we can assess the effectiveness of our approach through a Faster R-CNN detection model. Performance results show improvements over the polyp detections while reducing the false-negative rate. The experimental results also show better recall metrics in comparison with both the original training set and other studies in the literature.

Conclusion

We demonstrate that our proposed method has the potential to increase the data variability and number of samples in a reduced polyp dataset, improving the polyp detection rate and recall values. These results open new possibilities for advancing the study and implementation of new methods to improve computer-assisted medical image analysis.

中文翻译：

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使用深度卷积神经网络改进结肠息肉检测的训练数据增强

背景

在过去几年中，息肉检测中最相关的结果是通过深度学习技术实现的。然而，该领域最常见的障碍是样本数量减少和缺乏数据可变性的小数据集。本文描述了一种使用公开可用的结肠镜数据集来减少这种限制并改善息肉检测结果的方法。

方法

为了解决这个问题，我们增加了原始数据集中图像的数量和种类。我们的方法包括将息肉添加到数据集图像中。开发的算法严格选择图像内的最佳区域以接收息肉。此过程保留了图像的真实特征，同时为训练目的创建了更多样的样本。我们的方法允许将现有息肉复制到新的非息肉状目标区域。我们还开发了一种策略，通过生成对抗神经网络生成新的和更多样的息肉。因此，开发的方法丰富了训练数据，自动创建带有适当标签的新样本。

结果

我们将建议的数据增强应用于结肠息肉数据集。因此，我们可以通过 Faster R-CNN 检测模型来评估我们方法的有效性。性能结果显示了对息肉检测的改进，同时降低了假阴性率。与原始训练集和文献中的其他研究相比，实验结果还显示了更好的召回指标。

结论

我们证明了我们提出的方法有可能在减少的息肉数据集中增加数据可变性和样本数量，从而提高息肉检测率和召回值。这些结果为推进新方法的研究和实施以改进计算机辅助医学图像分析开辟了新的可能性。