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Spatio-Temporal Convolutional LSTMs for Tumor Growth Prediction by Learning 4D Longitudinal Patient Data [article]

Ling Zhang, Le Lu, Xiaosong Wang, Robert M. Zhu, Mohammadhadi Bagheri, Ronald M. Summers, Jianhua Yao
2019 arXiv   pre-print
We extend ConvLSTM into the spatio-temporal domain (ST-ConvLSTM) by jointly learning the inter-slice 3D contexts and the longitudinal or temporal dynamics from multiple patient studies.  ...  However, current 2D patch-based modeling approaches cannot make full use of the spatio-temporal imaging context of the tumor's longitudinal 4D (3D + time) data.  ...  Fig. 2 . 2 Left: The proposed Spatio-Temporal Convolutional LSTM (ST-ConvLSTM, or ST-CLSTM) network for learning of 4D medical imaging representations to predict tumor growth or segment object.  ... 
arXiv:1902.08716v2 fatcat:vm4274fsfndbzpwyt4wr2h2k4m

Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review

Zhao Zhang, Guangfei Li, Yong Xu, Xiaoying Tang
2021 Diagnostics  
An in-depth understanding of the principles and applications of magnetic resonance imaging (MRI), machine learning (ML), and deep learning (DL) is fundamental for developing AI-based algorithms that can  ...  Artificial intelligence (AI) for medical imaging is a technology with great potential.  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/diagnostics11081402 fatcat:mmouz5fb2ngzbe7jj2fyi5xpsy

AI and Medical Imaging Informatics: Current Challenges and Future Directions

Andreas S. Panayides, Amir Amini, Nenad Filipovic, Ashish Sharma, Sotirios Tsaftaris, Alistair Young, David J. Foran, Nhan Do, Spyretta Golemati, Tahsin Kurc, Kun Huang, Konstantina S. Nikita (+4 others)
2020 IEEE journal of biomedical and health informatics  
More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context  ...  of AI in big healthcare data analytics.  ...  The metabolic influence of oxygen, glucose and lactate is incorporated in multicompartment models of tumor spatio-temporal evolution, enabling the formation of cell populations with different metabolic  ... 
doi:10.1109/jbhi.2020.2991043 pmid:32609615 pmcid:PMC8580417 fatcat:dcaefxwwqjfwla5asin34x2hxm

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions [article]

Fouzia Altaf, Syed M. S. Islam, Naveed Akhtar, Naeem K. Janjua
2019 arXiv   pre-print
provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future.  ...  Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging.  ...  [191] proposed a stacked bidirectional convolutional LSTM (C-LSTM) network for the reconstruction of 3D images from the 4D spatio-temporal data.  ... 
arXiv:1902.05655v1 fatcat:mjplenjrprgavmy5ssniji4cam

An overview of deep learning in medical imaging focusing on MRI

Alexander Selvikvåg Lundervold, Arvid Lundervold
2018 Zeitschrift für Medizinische Physik  
to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical  ...  imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.  ...  Our work was financially supported by the Bergen Research Foundation through the project "Computational medical imaging and machine learning -methods, infrastructure and applications".  ... 
doi:10.1016/j.zemedi.2018.11.002 fatcat:kkimovnwcrhmth7mg6h6cpomjm

2021 AIUM Award Winners

2021 Journal of ultrasound in medicine  
high spatio-temporal resolution for detectability with a 8 to 22-MHz transducer and application-sensitive algorithms for flow assessment.  ...  convolution followed by multiple dilated convolutions.  ...  We will further develop virtual journal club and scanning sessions by attracting more participants and measuring learning outcomes.  ... 
doi:10.1002/jum.15752 fatcat:v4nx5fvjwndrzfppaiaylgon64

Machine learning for magnetic resonance image reconstruction and analysis

Chen Qin, Daniel Rueckert
2020
A convolutional recurrent neural network architecture (CRNN-MRI) is developed, where it embeds the iterative optimisation process in a learning setting and exploits the temporal redundancies of cardiac  ...  A joint learning framework for cardiac segmentation and motion estimation is proposed, which can provide multi-task predictions simultaneously.  ...  [190] proposed to use B-spline based FFDs for the spatio-temporal alignment of cardiac MR sequences, where the 4D B-spline was separated into spatial and temporal components.  ... 
doi:10.25560/79301 fatcat:bdyyeutojjas3cppkur6xn6aqe