Synthesizing Cell Protein data for Human Protein Cell Profiling Using Dual Deep Generative Modeling

Reviewed
Rakesh Ranjan, Sozo Inoue, Tom Shibata,
International Conference on Imaging, Vision & Pattern Recognition (IVPR)
(Not Available)
(Not Available)
1-6
2020-08-26
Kitakyushu
https://ieeexplore.ieee.org/document/9306574
To understand the biology of health, and how molecular dysfunction leads to disease, knowledge of the human cell is essential. The protein is the core unit of the human body made from trillions of cells, forming the body's various tissues. These tissues come together to create human organs. It is essential to understand the Spatio-temporal distribution of proteins in cells and to investigate human RNA-sequencing for human genes characterization. For this, it requires a massive amount of annotated data. However, due to many considerations like the high cost of data sample collection, lack of data sample availability, and lawful clauses for patient privacy, the majority of medical data is out of reach for general public research. In this study, we propose a new dual deep generative method for synthesizing human cell protein images by using the Generative Adversarial Network technique. Specifically, for that, we pair original cell protein images with their respective Cell-protein-tree. These pairs are then used to learn the mapping from a binary cell protein to a new cell protein image. For this purpose, we use an image-to-image translation technique based on adversarial learning. The generated cell protein images are expected to preserve the structural and visual quality of the training images. Visual and quantitative analysis of the experimental results demonstrates that the synthesized data are preserving the desired quality while maintaining the different forms of original data. Contribution-We have proposed a new dual deep generative model for synthesizing cell protein data.

Data Files

No records to display