scholarly journals Edge-Sensitive Left Ventricle Segmentation Using Deep Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2375
Author(s):  
Jingjing Xiong ◽  
Lai-Man Po ◽  
Kwok Wai Cheung ◽  
Pengfei Xian ◽  
Yuzhi Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Left Ventricle ◽  
Autonomous Driving ◽  
Learning Methods ◽  
Proposed Model ◽  
Markov Decision ◽  
Edge Points ◽  
Cardiac Diagnosis ◽  
Ventricle Segmentation

Deep reinforcement learning (DRL) has been utilized in numerous computer vision tasks, such as object detection, autonomous driving, etc. However, relatively few DRL methods have been proposed in the area of image segmentation, particularly in left ventricle segmentation. Reinforcement learning-based methods in earlier works often rely on learning proper thresholds to perform segmentation, and the segmentation results are inaccurate due to the sensitivity of the threshold. To tackle this problem, a novel DRL agent is designed to imitate the human process to perform LV segmentation. For this purpose, we formulate the segmentation problem as a Markov decision process and innovatively optimize it through DRL. The proposed DRL agent consists of two neural networks, i.e., First-P-Net and Next-P-Net. The First-P-Net locates the initial edge point, and the Next-P-Net locates the remaining edge points successively and ultimately obtains a closed segmentation result. The experimental results show that the proposed model has outperformed the previous reinforcement learning methods and achieved comparable performances compared with deep learning baselines on two widely used LV endocardium segmentation datasets, namely Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset, and Sunnybrook 2009 dataset. Moreover, the proposed model achieves higher F-measure accuracy compared with deep learning methods when training with a very limited number of samples.

Left ventricle segmentation for cine MR using deep learning

2021 ◽  
pp. 37-57
Author(s):  
Hisham Abdeltawab ◽  
Fahmi Khalifa ◽  
Fatma Taher ◽  
Mohammed Ghazal ◽  
Ali H. Mahmoud ◽  
...  
Keyword(s):  
Deep Learning ◽  
Left Ventricle ◽  
Cine Mr ◽  
Ventricle Segmentation

scholarly journals MFP-Unet: A novel deep learning based approach for left ventricle segmentation in echocardiography

2019 ◽  
Vol 67 ◽  
pp. 58-69 ◽  
Author(s):  
Shakiba Moradi ◽  
Mostafa Ghelich Oghli ◽  
Azin Alizadehasl ◽  
Isaac Shiri ◽  
Niki Oveisi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Left Ventricle ◽  
Ventricle Segmentation

2D left ventricle segmentation using deep learning

2017 ◽  
Author(s):  
Erik Smistad ◽  
Andreas Ostvik ◽  
Bjorn Olav Haugen ◽  
Lasse Lovstakken
Keyword(s):  
Deep Learning ◽  
Left Ventricle ◽  
Ventricle Segmentation

scholarly journals MAPS: Multi-Agent reinforcement learning-based Portfolio management System.

2020 ◽  
Author(s):  
Jinho Lee ◽  
Raehyun Kim ◽  
Seok-Won Yi ◽  
Jaewoo Kang
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Portfolio Management ◽  
Management System ◽  
Investment Strategy ◽  
Sharpe Ratio ◽  
Training Procedure ◽  
Learning Methods ◽  
Multi Agent ◽  
Diversified Portfolio

Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest. Most existing deep learning methods focus on proposing an optimal model or network architecture by maximizing return. However, these models often fail to consider and adapt to the continuously changing market conditions. In this paper, we propose the Multi-Agent reinforcement learning-based Portfolio management System (MAPS). MAPS is a cooperative system in which each agent is an independent "investor" creating its own portfolio. In the training procedure, each agent is guided to act as diversely as possible while maximizing its own return with a carefully designed loss function. As a result, MAPS as a system ends up with a diversified portfolio. Experiment results with 12 years of US market data show that MAPS outperforms most of the baselines in terms of Sharpe ratio. Furthermore, our results show that adding more agents to our system would allow us to get a higher Sharpe ratio by lowering risk with a more diversified portfolio.

scholarly journals Ordering-Based Causal Discovery with Reinforcement Learning

2021 ◽  
Author(s):  
Xiaoqiang Wang ◽  
Yali Du ◽  
Shengyu Zhu ◽  
Liangjun Ke ◽  
Zhitang Chen ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Directed Graphs ◽  
Real Data ◽  
Causal Discovery ◽  
Small Scale ◽  
Search Problem ◽  
Data Sets ◽  
Proposed Model ◽  
Markov Decision ◽  
Improved Performance

It is a long-standing question to discover causal relations among a set of variables in many empirical sciences. Recently, Reinforcement Learning (RL) has achieved promising results in causal discovery from observational data. However, searching the space of directed graphs and enforcing acyclicity by implicit penalties tend to be inefficient and restrict the existing RL-based method to small scale problems. In this work, we propose a novel RL-based approach for causal discovery, by incorporating RL into the ordering-based paradigm. Specifically, we formulate the ordering search problem as a multi-step Markov decision process, implement the ordering generating process with an encoder-decoder architecture, and finally use RL to optimize the proposed model based on the reward mechanisms designed for each ordering. A generated ordering would then be processed using variable selection to obtain the final causal graph. We analyze the consistency and computational complexity of the proposed method, and empirically show that a pretrained model can be exploited to accelerate training. Experimental results on both synthetic and real data sets shows that the proposed method achieves a much improved performance over existing RL-based method.

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