Machine Learning for H-FIRE Protocols: Tuning Parameters for High-Frequency Irreversible Electroporation by Machine Learning-Reference-Cited by-同舟云学术

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Machine Learning for H-FIRE Protocols: Tuning Parameters for High-Frequency Irreversible Electroporation by Machine Learning

Published:2021-09 Issue:9 Volume:22 Page:42-59
ISSN:1527-3342
Container-title:IEEE Microwave Magazine
language:
Short-container-title:IEEE Microwave

Author:

Zappatore Marco,Cerfeda Giulia,Merla Caternina,Tarricone Luciano^ORCID

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Radiation

Link

http://xplorestaging.ieee.org/ielx7/6668/9503432/09503474.pdf?arnumber=9503474

Cited by 10 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. High-Frequency Irreversible Electroporation: Optimum Parameter Prediction via Machine-Learning;IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology;2024-09

2. Tissue Nanotransfection Silicon Chip and Related Electroporation-Based Technologies for In Vivo Tissue Reprogramming;Nanomaterials;2024-01-19

3. Optimizing Electroporation Responses in Genetically Engineered HEK Cells: An Ensemble Learning Approach;IFMBE Proceedings;2024

4. Tumor location on electroporation therapies by means of multi-electrode structures and machine learning;Bioelectrochemistry;2023-12

5. Optimization of Ablation Area and Electrode Positioning in High Frequency Irreversible Electroporation via Machine Learning;2023 IEEE MTT-S International Microwave Biomedical Conference (IMBioC);2023-09-11

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