Exploiting Computation Power of Blockchain for Biomedical Image Segmentation

Biomedical image segmentation based on Deep neural network (DNN) is a promising approach that assists clinical diagnosis. This approach demands enormous computation power because these DNN models are complicated, and the size of the training data is usually very huge. As blockchain technology based on Proof-of-Work (PoW) has been widely used, an immense amount of computation power is consumed to maintain the PoW consensus. In this paper, we propose a design to exploit the computation power of blockchain miners for biomedical image segmentation, which lets miners perform image segmentation as the Proof-of-Useful-Work (PoUW) instead of calculating useless hash values. This work distinguishes itself from other PoUW by addressing various limitations of related others. As the overhead evaluation shown in Section 5 indicates, for U-net and FCN, the average overhead of digital signature is 1.25 seconds and 0.98 seconds, respectively, and the average overhead of network is 3.77 seconds and 3.01 seconds, respectively. These quantitative experiment results prove that the overhead of the digital signature and network is small and comparable to other existing PoUW designs. paper link

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BibTex: @InProceedings{Li_2019_CVPR_Workshops, author = {Li, Boyang and Chenli, Changhao and Xu, Xiaowei and Jung, Taeho and Shi, Yiyu}, title = {Exploiting Computation Power of Blockchain for Biomedical Image Segmentation}, booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2019} }