This paper focuses on the problem of lung nodule image classification, which plays a key role in lung cancer early diagnosis. In this work, we propose a novel model for lung nodule image feature representation that incorporates both local and global characters. First, lung nodule images are divided into local patches with Superpixel. Then these patches are transformed into fixed-length local feature vectors using unsupervised deep autoencoder (DAE). The visual vocabulary is constructed based on the local features and bag of visual words (BOVW) is used to describe the global feature representation of lung nodule image. Finally, softmax algorithm is employed for lung nodule type classification, which can assemble the whole training process as an end-to-end mode. Comprehensive evaluations are conducted on the widely used public available ELCAP lung image database. Experimental results with regard to different parameter setting, data augmentation, model sparsity, classifier algorithms, and model ensemble validate the effectiveness of our proposed approach.

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Electrocardiogram (ECG) data classification is a hot research area for its application in medical information processing. However, insufficient data, privacy preserve, and local deployment are still challenging difficulties. To address these problems, a novel personalized federated learning method for ECG classification is proposed in this paper. First, a global model is trained with federated learning framework on multiple local data clients. Then, we use the global model and private data to train the local model. To reduce the feature inconsistency between global and private local data and for better fitting the private local data, a novel ”feature alignment” module is devised to guarantee the uniformity, which contains two parts, global alignment and local alignment, respectively. For global alignment, the graph metric of batch data is used to constrain the dissimilarity between features generated by the global model and local model. For local alignment, triplet loss is adopted to increase discriminative ability for local private data. Comprehensive experiments on our collected dataset are evaluated. The results show that the proposed method can be better adapted to local data and exhibit superior ability of generalization.

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