This paper proposes transfer learning with spatiotemporal feature analysis using public facial images to build an automatic detection of facial paralysis caused by acute stroke. The overall process includes 1) facial detection and alignment network to extract major regions from the face, 2) transfer learning with feature extraction networks and gated recurrent unit, and 3) a classifier evaluating facial paralysis. We leveraged the Korean facial image data (K-FACE) from the public AI Hub to compensate for the insufficient data representing acute stroke symptoms. The experiment analyzed the effect of transfer learning and time series analysis using a gated recurrent unit with the deep learning models based on MobileNetV2, VGG16, and DenseNet121. Utilizing a facial big data system, transfer learning with spatiotemporal features showed a prominent performance with an accuracy of 0.925 and AUC of 0.924, which indicates the feasibility of real-time detection of stroke in daily living.

Medical big data has become important as many hospitals have been collecting massive amounts of medical information in daily treatment. We investigated the architecture of federated learning to construct the detection model of disease with blood test data formatted in flow cytometry standards to facilitate multi-site medical research. The big data characteristics of raw information in blood tests and privacy problems in sharing patient data make it hard to collect and share data into the central site to construct the generalized detection model. In this paper, we introduce the work-in-progress study, FedM-FCM, the federated learning of flow cytometry analysis with the pipeline from the data sources to the domain-shifted distribution of the federated learning model. We compose the major components of FedM-FCM with data representation of multi-dimensional flow cytometry, adoption of neural network models based on the data representation, and aggregation and distribution of learning parameters across the participating hospitals without data sharing.