Real-time monitoring radiofrequency ablation using tree-based ensemble learning models.

Emre BeslerYearnchee Curtis WangTerence C ChanAlan V Sahakian

Published in: International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group (2019)

It is shown that an optimal pair of hardware setup and ML algorithm (Adaboost) is able to control the ablation by estimating the lesion depth within a test average of 0.3mm while keeping the estimation time within 10ms on a ×86-64 workstation.

Keyphrases

radiofrequency ablation
neural network
mass spectrometry
machine learning
multiple sclerosis
ms ms
deep learning
optical coherence tomography
convolutional neural network