May 25, 2022, 1:13 a.m. | Michel Pohl, Mitsuru Uesaka, Hiroyuki Takahashi, Kazuyuki Demachi, Ritu Bhusal Chhatkuli

cs.CV updates on arXiv.org arxiv.org

During lung radiotherapy, the position of infrared reflective objects on the
chest can be recorded to estimate the tumor location. However, radiotherapy
systems have a latency inherent to robot control limitations that impedes the
radiation delivery precision. Prediction with online learning of recurrent
neural networks (RNN) allows for adaptation to non-stationary respiratory
signals, but classical methods such as RTRL and truncated BPTT are respectively
slow and biased. This study investigates the capabilities of unbiased online
recurrent optimization (UORO) to forecast …

arxiv cancer lung cancer network neural network optimization prediction recurrent neural network unbiased

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Program Control Data Analyst

@ Ford Motor Company | Mexico

Vice President, Business Intelligence / Data & Analytics

@ AlphaSense | Remote - United States