Explanations of a model's biases or predictions are essential to medical image analysis. Yet, explainable machine learning approaches for medical image analysis are challenged by needs to preserve privacy of patient data, and by current trends in deep learning to use unsustainably large models and large datasets. We propose DeepFixCX for explainable and privacy-preserving medical image compression that is nimble and performant. We contribute a review of the field and a conceptual framework for simultaneous privacy and explainability via tools of compression. DeepFixCX compresses images without learning by removing or obscuring spatial and edge information. DeepFixCX is ante-hoc explainable and gives privatized post hoc explanations of spatial and edge bias without accessing the original image. DeepFixCX privatizes images to prevent image reconstruction and mitigate patient re-identification. DeepFixCX is nimble. Compression can occur on a laptop CPU or GPU to compress and privatize 1700 images per second of size 320 × 320. DeepFixCX enables use of low memory MLP classifiers for vision data; permitting small performance loss gives end-to-end MLP performance over 70× faster and batch size over 100× larger. DeepFixCX consistently improves predictive classification performance of a Deep Neural Network (DNN) by 0.02 AUC ROC on Glaucoma and Cervix Type detection datasets, and can improve multi-label chest x-ray classification performance in seven of 10 tested settings. In all three datasets, compression to less than 5% of original number of pixels gives matching or improved performance. Our main novelty is to define an explainability versus privacy problem and address it with lossy compression.

中文翻译：

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DeepFixCX：用于医学图像分析的可解释的隐私保护图像压缩

对模型偏差或预测的解释对于医学图像分析至关重要。然而，用于医学图像分析的可解释机器学习方法受到保护患者数据隐私的需求以及当前深度学习使用不可持续的大型模型和大型数据集的趋势的挑战。我们提出DeepFixCX来实现灵活且高性能的可解释且保护隐私的医学图像压缩。我们对该领域进行了回顾，并通过压缩工具提供了同时实现隐私和可解释性的概念框架。DeepFixCX通过删除或模糊空间和边缘信息来压缩图像，无需学习。深度修复CX是事前可解释的，并且在不访问原始图像的情况下给出空间和边缘偏差的私有化事后解释。DeepFixCX将图像私有化，以防止图像重建并减少患者重新识别。DeepFixCX非常灵活。压缩可以在笔记本电脑 CPU 或 GPU 上进行，以每秒压缩和私有化 1700 张尺寸为 320 × 320 的图像。DeepFixCX可以使用低内存 MLP 分类器来处理视觉数据；允许较小的性能损失可使端到端 MLP 性能提高 70 倍以上，批量大小增大 100 倍以上。深度修复CX在青光眼和宫颈类型检测数据集上，深度神经网络 (DNN) 的预测分类性能持续提高 0.02 AUC ROC，并且可以在 10 个测试设置中的 7 个设置中提高多标签胸部 X 射线分类性能。在所有三个数据集中，压缩到原始像素数的 5% 以下可提供匹配或改进的性能。我们的主要新颖之处是定义可解释性与隐私问题，并通过有损压缩来解决它。