Version 1
: Received: 29 July 2024 / Approved: 30 July 2024 / Online: 30 July 2024 (13:48:31 CEST)
How to cite:
Berezsky, O.; Pitsun, O.; Berezkyy, M.; Batko, Y.; Melnyk, G. MLOps Approach for Automatic Image Segmentation Based on U-Net. Preprints2024, 2024072447. https://doi.org/10.20944/preprints202407.2447.v1
Berezsky, O.; Pitsun, O.; Berezkyy, M.; Batko, Y.; Melnyk, G. MLOps Approach for Automatic Image Segmentation Based on U-Net. Preprints 2024, 2024072447. https://doi.org/10.20944/preprints202407.2447.v1
Berezsky, O.; Pitsun, O.; Berezkyy, M.; Batko, Y.; Melnyk, G. MLOps Approach for Automatic Image Segmentation Based on U-Net. Preprints2024, 2024072447. https://doi.org/10.20944/preprints202407.2447.v1
APA Style
Berezsky, O., Pitsun, O., Berezkyy, M., Batko, Y., & Melnyk, G. (2024). MLOps Approach for Automatic Image Segmentation Based on U-Net. Preprints. https://doi.org/10.20944/preprints202407.2447.v1
Chicago/Turabian Style
Berezsky, O., Yuriy Batko and Grygoriy Melnyk. 2024 "MLOps Approach for Automatic Image Segmentation Based on U-Net" Preprints. https://doi.org/10.20944/preprints202407.2447.v1
Abstract
The usage of classic segmentation methods has drastically decreased recently. Implementation of convolutional neural networks and their modifications leads to significantly improved results. With the help of U-net, you can perform automatic segmentation. However, this requires significantly more hardware and software resources. The use of cloud computing now provides a wide range of possibilities, which allows processing and sharing the obtained experience and results to improve the quality of diagnosis. This article presents an approach to implementing MLOPS for automatic image segmentation using U-net technology. The approach's key feature is the developed software block that allows the creation of a dataset based on given rules. The infrastructure also supports automatically deploying the necessary environment on the cloud, particularly on DigitalOcean.
Keywords
MLOps; infrastructure as code; immunohistochemical images
Subject
Computer Science and Mathematics, Software
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
