  Recently, deep convolutional neural networks (CNNs) have achieved great
success in pathological image classification. However, due to the limited
number of labeled pathological images, there are still two challenges to be
addressed: (1) overfitting: the performance of a CNN model is undermined by the
overfitting due to its huge amounts of parameters and the insufficiency of
labeled training data. (2) privacy leakage: the model trained using a
conventional method may involuntarily reveal the private information of the
patients in the training dataset. The smaller the dataset, the worse the
privacy leakage. To tackle the above two challenges, we introduce a novel
stochastic gradient descent (SGD) scheme, named patient privacy preserving SGD
(P3SGD), which performs the model update of the SGD in the patient level via a
large-step update built upon each patient's data. Specifically, to protect
privacy and regularize the CNN model, we propose to inject the well-designed
noise into the updates. Moreover, we equip our P3SGD with an elaborated
strategy to adaptively control the scale of the injected noise. To validate the
effectiveness of P3SGD, we perform extensive experiments on a real-world
clinical dataset and quantitatively demonstrate the superior ability of P3SGD
in reducing the risk of overfitting. We also provide a rigorous analysis of the
privacy cost under differential privacy. Additionally, we find that the models
trained with P3SGD are resistant to the model-inversion attack compared with
those trained using non-private SGD.
