论文标题
通过深色弥合自然图像和医学图像之间的差距
Bridging the gap between Natural and Medical Images through Deep Colorization
论文作者
论文摘要
深度学习是通过在大型数据集上进行培训而蓬勃发展的。但是，在许多应用中，就医学图像诊断而言，由于隐私，缺乏获取同质性和注释成本，获得大量数据仍然是过于刺激的。在这种情况下，从自然图像收集中转移学习是一种标准实践，试图通过预算的模型微调立即解决形状，质地和颜色差异。在这项工作中，我们建议解散这些挑战，并设计一个专注于颜色适应的专用网络模块。我们将颜色模块的学习与不同分类主干的转移学习结合在一起，获得了X射线映像上的端到端，易于培训的架构，用于诊断图像识别。广泛的实验表明，在数据稀缺的情况下，我们的方法特别有效，并为在多个医疗数据集中进一步传输学习的颜色信息提供了新的途径。
Deep learning has thrived by training on large-scale datasets. However, in many applications, as for medical image diagnosis, getting massive amount of data is still prohibitive due to privacy, lack of acquisition homogeneity and annotation cost. In this scenario, transfer learning from natural image collections is a standard practice that attempts to tackle shape, texture and color discrepancies all at once through pretrained model fine-tuning. In this work, we propose to disentangle those challenges and design a dedicated network module that focuses on color adaptation. We combine learning from scratch of the color module with transfer learning of different classification backbones, obtaining an end-to-end, easy-to-train architecture for diagnostic image recognition on X-ray images. Extensive experiments showed how our approach is particularly efficient in case of data scarcity and provides a new path for further transferring the learned color information across multiple medical datasets.
