Lite-UNet: A lightweight and efficient network for cell localization-Reference-Cited by-同舟云学术

Lite-UNet: A lightweight and efficient network for cell localization

Published:2024-03 Issue: Volume:129 Page:107634
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Li Bo^ORCID,Zhang Yong^ORCID,Ren Yunhan,Zhang Chengyang,Yin Baocai

Funder

National Natural Science Foundation of China

Beijing Municipal Commission of Education

National Key Research and Development Program of China

National Key Research and Development Program of China Stem Cell and Translational Research

Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference43 articles.

1. Machine learning approach of automatic identification and counting of blood cells;Alam;Healthc. Technol. Lett.,2019

2. Saliency and ballness driven deep learning framework for cell segmentation in bright field microscopic images;Asha;Eng. Appl. Artif. Intell.,2023

3. Atwood, J., Towsley, D., 2016. Diffusion-convolutional neural networks. In: Proceedings of the Advances in Neural Information Processing Systems, Vol. 29.

4. Segmentation of medical images using an attention embedded lightweight network;Chen;Eng. Appl. Artif. Intell.,2022

5. Cell localization and counting using direction field map;Chen;IEEE J. Biomed. Health Inf.,2021

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