What is it about?
In this a new learning or training hybrid approach is proposed based on the Google's inception module and a U-Net topology and a segmentation loss function based on Jaccard index, dice coefficient and binary cross entropy metrics
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Photo by Hitesh Choudhary on Unsplash
Why is it important?
Nuclei are the first things that gets affected during illness or disease. The automated detection of nuclei can help the doctors in faster diagnosis and treatment of the illness.
Perspectives
We hope to improve upon the quality of living by contributing the artificial intelligent techniques in the healthcare domain. This article aims to assist the process of diagnosis for faster cures and treatment.
Nairnder Singh Punn
Indian Institute of Information Technology Allahabad
Read the Original
This page is a summary of: Inception U-Net Architecture for Semantic Segmentation to Identify Nuclei in Microscopy Cell Images, ACM Transactions on Multimedia Computing Communications and Applications, April 2020, ACM (Association for Computing Machinery),
DOI: 10.1145/3376922.
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