Time-series medical images are an important type of medical data that contain rich temporal and spatial information. As a state-of-the-art, computer-aided diagnosis (CAD) algorithms are usually used on these image sequences to improve analysis accuracy. However, such CAD algorithms are often required to upload medical images to honest-but-curious servers, which introduces severe privacy concerns. To preserve privacy, the existing CAD algorithms support analysis on each encrypted image but not on the whole encrypted image sequences, which leads to the loss of important temporal information among frames. To meet this challenge, a convolutional-LSTM network, named HE-CLSTM, is proposed for analyzing time-series medical images encrypted by a fully homomorphic encryption mechanism. Specifically, several convolutional blocks are constructed to extract discriminative spatial features, and LSTM-based sequence analysis layers (HE-LSTM) are leveraged to encode temporal information from the encrypted image sequences. Moreover, a weighted unit and a sequence voting layer are designed to incorporate both spatial and temporal features with different weights to improve performance while reducing the missed diagnosis rate. The experimental results on two challenging benchmarks (a Cervigram dataset and the BreaKHis public dataset) provide strong evidence that our framework can encode visual representations and sequential dynamics from encrypted medical image sequences; our method achieved AUCs above 0.94 both on the Cervigram and BreaKHis datasets, constituting a significant margin of statistical improvement compared with several competing methods.

中文翻译：

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使用混合深度学习框架的隐私保护时间序列医学图像分析

时间序列医学图像是一种重要的医学数据类型，包含丰富的时空信息。作为最先进的技术，计算机辅助诊断 (CAD) 算法通常用于这些图像序列以提高分析准确性。但是，通常需要此类 CAD 算法将医学图像上传到诚实但好奇的服务器，这会带来严重的隐私问题。为了保护隐私，现有的 CAD 算法支持对每个加密图像进行分析，但不支持对整个加密图像序列进行分析，这会导致帧之间重要时间信息的丢失。为了应对这一挑战，提出了一种名为 HE-CLSTM 的卷积 LSTM 网络，用于分析由完全同态加密机制加密的时间序列医学图像。具体来说，构建了几个卷积块以提取有区别的空间特征，并利用基于 LSTM 的序列分析层 (HE-LSTM) 对来自加密图像序列的时间信息进行编码。此外，加权单元和序列投票层旨在结合具有不同权重的空间和时间特征，以提高性能，同时降低漏诊率。两个具有挑战性的基准（Cervigram 数据集和 BreaKHis 公共数据集）的实验结果提供了强有力的证据，证明我们的框架可以编码来自加密医学图像序列的视觉表示和序列动态；我们的方法在 Cervigram 和 BreaKHis 数据集上均实现了高于 0.94 的 AUC，与几种竞争方法相比，构成了显着的统计改进余量。