压缩20倍后医学影像分割精度不变，模型能互用
The Effect of Lossy Compression on 3D Medical Images Segmentation with Deep Learning
- 用深度网络在压缩数据上训练，效果不降
- 20倍压缩下分割准确率无明显下降
- 压缩与未压缩数据间模型可通用
图像压缩是降低存储成本、提升互联网传输速度的关键技术。尽管深度学习在自然图像中广泛采用有损压缩，但在3D医学影像中仍不常见。本研究基于三个CT数据集（17项任务）和一个MRI数据集（3项任务），证明了高达20倍的有损压缩对深度神经网络（DNN）进行分割任务无负面影响。此外，我们验证了在压缩数据上训练的DNN模型可直接用于未压缩数据预测，反之亦然，且性能无显著退化。
原文摘要 · Abstract (English)
Image compression is a critical tool in decreasing the cost of storage and improving the speed of transmission over the internet. While deep learning applications for natural images widely adopts the usage of lossy compression techniques, it is not widespread for 3D medical images. Using three CT datasets (17 tasks) and one MRI dataset (3 tasks) we demonstrate that lossy compression up to 20 times have no negative impact on segmentation quality with deep neural networks (DNN). In addition, we demonstrate the ability of DNN models trained on compressed data to predict on uncompressed data and vice versa with no quality deterioration.
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