LLTO: Towards efficient lesion localization based on template occlusion strategy in intelligent diagnosis

Unsupervised domain adaptation model for lesion detection in retinal OCT images

Background and objective.Optical coherence tomography (OCT) is one of the most used retinal imaging modalities in the clinic as it can provide high-resolution anatomical images. The huge number of OCT images has significantly advanced the development of deep learning methods for automatic lesion detection to ease the doctor's workload. However, it has been frequently revealed that the deep neural network model has difficulty handling the domain discrepancies, which widely exist in medical images captured from different devices. Many works have been proposed to solve the domain shift issue in deep learning tasks such as disease classification and lesion segmentation, but few works focused on lesion detection, especially for OCT images.Methods.In this work, we proposed a faster-RCNN based, unsupervised domain adaptation model to address the lesion detection task in cross-device retinal OCT images. The domain shift is minimized by reducing the image-level shift and instance-level shift at the same time. We combined a domain classifier with a Wasserstein distance critic to align the shifts at each level.Results.The model was tested on two sets of OCT image data captured from different devices, obtained an average accuracy improvement of more than 8% over the method without domain adaptation, and outperformed other comparable domain adaptation methods.Conclusion.The results demonstrate the proposed model is more effective in reducing the domain shift than advanced methods.

Local privacy protection classification based on human-centric computing

Human-centric computing is becoming an important part of data-driven artificial intelligence (AI) and the importance of data mining under Human-centric computing is getting more and more attention. The rapid development of machine learning has gradually increased its ability to mine data. In this paper, privacy protection is combined with machine learning, in which a logistic regression is adopted for local differential privacy protection to achieves classification task utilizing noise addition and feature selection. The design idea is mainly divided into three parts: noise addition, feature selection and logistic regression. In the part of noise addition, the way of adding noise using Laplace mechanism to original data achieves the purpose of disturbing data. The part of feature selection is to highlight the impact of noised data on the classifier. The part of logistic regression is to use logistic regression to implement classification task. The experimental results show that an accuracy of 85.7% can be achieved for the privacy data by choosing appropriate regularization coefficients.