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Data and Physics Driven Learning Models for Fast MRI – Fundamentals and Methodologies from CNN, GAN to Attention and Transformers [article]

Jiahao Huang, Yingying Fang, Yang Nan, Huanjun Wu, Yinzhe Wu, Zhifan Gao, Yang Li, Zidong Wang, Pietro Lio, Daniel Rueckert, Yonina C. Eldar, Guang Yang
2022 arXiv   pre-print
and transformer based models for speeding up MRI reconstruction, and (3) detail the research in coupling physics and data driven models for MRI acceleration.  ...  This article aims to (1) introduce the deep learning based data driven techniques for fast MRI including convolutional neural network and generative adversarial network based methods, (2) survey the attention  ...  MR PHYSICS FOR DATA DRIVEN MODELS A.  ... 
arXiv:2204.01706v1 fatcat:7vwd52c23faglm2c4zmcqwzjtu

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  ...  Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry.  ...  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

Deep learning in medical image registration

Xiang Chen, Andres Diaz-Pinto, Nishant Ravikumar, Alejandro Frangi
2020 Progress in Biomedical Engineering  
To this end, the main contributions of this paper are: (a) discussion of all deep learning-based medical image registration papers published since 2013 with significant methodological and/or functional  ...  of unmet clinical needs and potential directions for future research in deep learning-based medical image registration.  ...  Acknowledgments The Royal Academy of Engineering supports the work of A F F through a Chair in Emerging Technologies (CiET1819\19) and the MedIAN Network (EP/N026993/1) funded by the Engineering and Physical  ... 
doi:10.1088/2516-1091/abd37c fatcat:74w7ra4f7nfrrpfk2ifvmijntq

The promise of artificial intelligence and deep learning in PET and SPECT imaging

Hossein Arabi, Azadeh AkhavanAllaf, Amirhossein Sanaat, Isaac Shiri, Habib Zaidi
2021 Physica medica (Testo stampato)  
A brief description of deep learning algorithms and the fundamental architectures used for these applications is also provided.  ...  Finally, the challenges, opportunities, and barriers to full-scale validation and adoption of AI-based solutions for improvement of image quality and quantitative accuracy of PET and SPECT images in the  ...  Acknowledgments This work was supported by the Swiss National Science Foundation under grant SNRF 320030_176052 and the Private Foundation of Geneva University Hospitals under grant RC-06-01.  ... 
doi:10.1016/j.ejmp.2021.03.008 pmid:33765602 fatcat:onw4fm22y5cxndxiwyy5bdw4t4

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.  ...  We draw on the insights from the sister research fields of Computer Vision, Pattern Recognition and Machine Learning etc.; where the techniques of dealing with such challenges have already matured, to  ...  This phenomenon is fundamental to any Machine Learning technique. A complex model inferred using a limited amount of data normally over-fits to the used data and performs poorly on any other data.  ... 
arXiv:1902.05655v1 fatcat:mjplenjrprgavmy5ssniji4cam

Potentials and caveats of AI in Hybrid Imaging

Lalith Kumar Shiyam Sundar, Otto Muzik, Irène Buvat, Luc Bidaut, Thomas Beyer
2020 Methods  
and modelling.  ...  information from large sets of multi-dimensional imaging data.  ...  This fundamental characteristic of GANs needs to be kept in mind when attempting to use this methodology in the context of abnormal patient data.  ... 
doi:10.1016/j.ymeth.2020.10.004 pmid:33068741 fatcat:cf64gu3vgbgxfls5gz75skiu74

Deep learning in medical imaging and radiation therapy

Berkman Sahiner, Aria Pezeshk, Lubomir M. Hadjiiski, Xiaosong Wang, Karen Drukker, Kenny H. Cha, Ronald M. Summers, Maryellen L. Giger
2018 Medical Physics (Lancaster)  
for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.  ...  The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies  ...  the use of the Radon transform for a known object and modeled different exposure conditions through adding noise to the data, for training a CNN to estimate high-dose projections from lowdose ones.  ... 
doi:10.1002/mp.13264 pmid:30367497 fatcat:bottst5mvrbkfedbuocbrstcnm

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

Fouzia Altaf, Syed M S Islam, Naveed Akhtar, Naeem Khalid Janjua
2019 IEEE Access  
promising directions for the Medical Imaging Community to fully harness deep learning in the future.  ...  We draw on the insights from the sister research fields of computer vision, pattern recognition, and machine learning, where the techniques of dealing with such challenges have already matured, to provide  ...  [218] proposed a CNN based technique for the 3D MRI abdomen image registration. They trained their model for the spatial transformation analysis of different images.  ... 
doi:10.1109/access.2019.2929365 fatcat:arimcbjaxrd3zcsjyzd7abjgd4

Deep Learning in Cardiology

Paschalis Bizopoulos, Dimitrios Koutsouris
2019 IEEE Reviews in Biomedical Engineering  
In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology.  ...  Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures.  ...  Medical procedures using machine learning are evolving from art to data-driven science, bringing insight from population-level data to the medical condition of the individual patient.  ... 
doi:10.1109/rbme.2018.2885714 fatcat:pa47trmskvflvig5cotth265q4

Small Sample Learning in Big Data Era [article]

Jun Shu, Zongben Xu, Deyu Meng
2018 arXiv   pre-print
This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures.  ...  The purpose is mainly to simulate human learning behaviors like recognition, generation, imagination, synthesis and analysis.  ...  A General Methodology for Concept Learning Concept learning aims to perform recognition or form new concepts (classes) from few observations (samples) through fast processing.  ... 
arXiv:1808.04572v3 fatcat:lqqzzrmgfnfb3izctvdzgopuny

An Application Independent Review of Multimodal 3D Registration Methods

Ph.D. E. Saiti, T. Theoharis
2020 Computers & graphics  
Multimodal registration is a special case where the data to be matched do not belong to the same modality and is challenging due to the diverse nature of the modalities involved which makes the creation  ...  and the potential for cross-fertilization.  ...  [196] utilized special data augmentation techniques called dithering and symmetrizing to train a CNN to learn a similarity metric from roughly aligned data.  ... 
doi:10.1016/j.cag.2020.07.012 fatcat:ggb4esbyhrfzxhcequym5dariu

NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction [article]

Liyue Shen, John Pauly, Lei Xing
2021 arXiv   pre-print
The method differs fundamentally from previous deep learning-based image reconstruction approaches in that NeRP exploits the internal information in an image prior, and the physics of the sparsely sampled  ...  No large-scale data is required to train the NeRP except for a prior image and sparsely sampled measurements.  ...  ACKNOWLEDGMENT The authors acknowledge the funding supports from the Stanford Bio-X Bowes Graduate Student Fellowship of Stanford University, NIH/NCI 1R01CA227713 and 1R01CA256890.  ... 
arXiv:2108.10991v1 fatcat:k3p2nzbfujhh7jndxnrlg5nxla

The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches [article]

Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Brian C Van Esesn, Abdul A S. Awwal, Vijayan K. Asari
2018 arXiv   pre-print
However, those papers have not discussed the individual advanced techniques for training large scale deep learning models and the recently developed method of generative models [1].  ...  (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL).  ...  Doctoral research scientist on deep Learning, computer vision for remote sensing and hyper spectral imaging (e-mail: pehedings@slu.edu). Brian C Van Esesn 3 and Abdul A S.  ... 
arXiv:1803.01164v2 fatcat:eo353y77tvckbdjcfexpaadeh4

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data.  ...  With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled.  ...  Some works have proposed CNNs and GANs for image super-resolution to transform a lower resolution brain intensity image to an image of higher resolution.  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y

Quantitative Phase Imaging and Artificial Intelligence: A Review [article]

YoungJu Jo, Hyungjoo Cho, Sang Yun Lee, Gunho Choi, Geon Kim, Hyun-seok Min, YongKeun Park
2018 arXiv   pre-print
Subsequently, the AI-assisted interrogation of QPI data using data-driven machine learning techniques results in a variety of biomedical applications. Also, machine learning enhances QPI itself.  ...  The fast and label-free nature of QPI enables the rapid generation of large-scale and uniform-quality imaging data in two, three, and four dimensions.  ...  Alternatively, now one can replace modeling by machine learning some aspects of the underlying physics in a data-driven manner.  ... 
arXiv:1806.03982v2 fatcat:bt2h4c63gnalhhsbm4c6zyuhtu
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