With the evolution of deep learning in the past decade, more biomedical related problems that seemed strenuous, are now feasible. The introduction of U-net and Mask R-CNN architectures has paved a way for many object detection and segmentation tasks in numerous applications ranging from security to biomedical applications. In the cell biology domain, light microscopy imaging provides a cheap and accessible source of raw data to study biological phenomena. By leveraging such data and deep learning techniques, human diseases can be easily diagnosed and the process of treatment development can be greatly expedited. In microscopic imaging, accurate segmentation of individual cells is a crucial step to allow better insight into cellular heterogeneity. To address the aforementioned challenges, DeepCeNS is proposed in this paper to detect and segment cells and nucleus in microscopic images. We have used EVICAN2 dataset which contains microscopic images from a variety of microscopes having numerous cell cultures, to evaluate the proposed pipeline. DeepCeNS outperforms EVICAN-MRCNN by a significant margin on the EVICAN2 dataset.

With the rapid growth of Internet media, content tagging has become an important topic with many multimedia understanding applications, including efficient organisation and search. Nevertheless, existing visual tagging approaches are susceptible to inherent privacy risks in which private information may be exposed unintentionally. The use of anonymisation and privacy-protection methods is desirable, but with the expense of task performance. Therefore, this paper proposes an end-to-end framework (SGTN) using Graph Transformer and Convolutional Networks to significantly improve classification and privacy preservation of visual data. Especially, we employ several mechanisms such as differential privacy based graph construction and noise-induced graph transformation to protect the privacy of knowledge graphs. Our approach unveils new state-of-the-art on MS-COCO dataset in various semi-supervised settings. In addition, we showcase a real experiment in the education domain to address the automation of sensitive document tagging. Experimental results show that our approach achieves an excellent balance of model accuracy and privacy preservation on both public and private datasets.