S-Space College of Engineering/Engineering Practice School (공과대학/대학원) Dept. of Mechanical Aerospace Engineering (기계항공공학부) Theses (Ph.D. / Sc.D._기계항공공학부)
Predicting Cellular Branching with Inverse Cellular Automata via Recurrent Neural Network
재귀 신경망이 적용된 역 셀룰러 오토마타 학습법을 활용한 세포 분지(分枝) 예측
- 공과대학 기계항공공학부
- Issue Date
- 서울대학교 대학원
- Cellular Automata; Biological Imaging; Image Prediction; Artificial Neural Network; Recurrent Neural Network
- 학위논문 (박사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2018. 2. 전누리.
- Biological processes are very complex so the predicting of such processes is
considered to be very difficult. When it comes to the image-based prediction, one of
the main reason of the predicting difficulty comes from the cost that gathering
enough visual data which is extremely expensive. In this thesis, we introduce a novel
method to reduce the cost of the prediction of cellular branching with Inverse
Cellular Automata (ICA). This method assumes the biological image sequence as the
set of cellular automata rules between t and t+1. With this assumption, single
biological image sequence can provide width * height * frame number of cellular
automata set of rule samples so they can be used as traditional Recurrent Neural
Network(RNN) based machine learning. Proposed method overcomes the problem
of lack of samples for machine learning in biological image predicting. We first
briefly review the traditional cellular automata and introduce the concept of the ICA.
And we will check the learnability with existing automata image rules. Finally, we
show the prediction images with proposed method.