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Recent advances and clinical applications of deep learning in medical image analysis [article]

Xuxin Chen, Ximin Wang, Ke Zhang, Roy Zhang, Kar-Ming Fung, Theresa C. Thai, Kathleen Moore, Robert S. Mannel, Hong Liu, Bin Zheng, Yuchen Qiu
2021 arXiv   pre-print
Despite the success, the further improvement of deep learning models in medical image analysis is majorly bottlenecked by the lack of large-sized and well-annotated datasets.  ...  Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical image analysis, which are summarized based on different application  ...  A more recent example is the Mean Teacher paradigm (Tarvainen and Valpola, 2017a), composed of a teacher model and a student model ( Figure 3 ).  ... 
arXiv:2105.13381v2 fatcat:2k342a6rhjaavpoa2qoqxhg5rq

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation [article]

Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey Chiang, Zhihao Wu, Xiaowei Ding
2020 arXiv   pre-print
However, rarely do we have a perfect training dataset, particularly in the field of medical imaging, where data and annotations are both expensive to acquire.  ...  data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations.  ...  Semi-supervised learning with pseudo annotations Semi-supervised learning with pseudo annotations consists of assigning pseudo annotations to unlabeled data and then training the segmentation model using  ... 
arXiv:1908.10454v2 fatcat:mjvfbhx75bdkbheysq3r7wmhdi

Curriculum Learning: A Survey [article]

Petru Soviany, Radu Tudor Ionescu, Paolo Rota, Nicu Sebe
2022 arXiv   pre-print
However, the necessity of finding a way to rank the samples from easy to hard, as well as the right pacing function for introducing more difficult data can limit the usage of the curriculum approaches.  ...  We construct a multi-perspective taxonomy of curriculum learning approaches by hand, considering various classification criteria.  ...  Acknowledgements The authors would like to thank the reviewers for their useful feedback.  ... 
arXiv:2101.10382v3 fatcat:doognr7ggfaalg7kd2i3n7s3jy

Deep Learning for Computational Cytology: A Survey [article]

Hao Jiang, Yanning Zhou, Yi Lin, Ronald CK Chan, Jiang Liu, Hao Chen
2022 arXiv   pre-print
We first introduce various deep learning methods, including fully supervised, weakly supervised, unsupervised, and transfer learning.  ...  Computational cytology is a critical, rapid-developing, yet challenging topic in the field of medical image computing which analyzes the digitized cytology image by computer-aided technologies for cancer  ...  Acknowledgments This work was supported by Beijing Institute of Collaborative Innovation Program (No. BICI22EG01).  ... 
arXiv:2202.05126v2 fatcat:d5ockk4ofjgv3oyxnuce4hmxpu

A Survey of Deep Active Learning [article]

Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B. Gupta, Xiaojiang Chen, Xin Wang
2021 arXiv   pre-print
Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters, so that the model learns how to extract high-quality features.  ...  This article is to fill this gap, we provide a formal classification method for the existing work, and a comprehensive and systematic overview.  ...  Structure comparison chart of VAAL [204] and TA-VAAL [112]. 1) VAAL uses labeled data and unlabeled data in a semi-supervised way to learn the latent representation space of the data, then selects the  ... 
arXiv:2009.00236v2 fatcat:zuk2doushzhlfaufcyhoktxj7e

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
2021 Information Fusion  
They have been applied to solve a variety of real-world problems in science and engineering.  ...  associated with UQ.  ...  Acknowledgment This work was partially supported by the Australian Research Council's Discovery Projects funding scheme (project DP190102181) and the Natural Sciences and Engineering Research Council of  ... 
doi:10.1016/j.inffus.2021.05.008 fatcat:yschhguyxbfntftj6jv4dgywxm

ijair-volume-6-issue-1-vii-january-march-2019 -HINDUSTAN BOOK.pdf

V. Thamilarasi
This paper experiments various basic image segmentation techniques for Lung Chest X-Ray images  ...  Semi-supervised learning can be used with methods such as classification, regression and prediction. Google Expander is the progression from semi-supervised learning [10] .  ...  Another real time scenario for semi supervised learning is document classification.  ... 
doi:10.6084/m9.figshare.20217722.v1 fatcat:l74ihuqhcvdtjomod3zdwzfniu

Opportunities and obstacles for deep learning in biology and medicine

Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin, Brian T. Do, Gregory P. Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M. Hoffman, Wei Xie, Gail L. Rosen (+24 others)
2018 Journal of the Royal Society Interface  
We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be  ...  Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records.  ...  for clarifying edits to the abstract and introduction and Robert Gieseke, Ruibang Luo, Stephen Ra, Sourav Singh and GitHub user snikumbh for correcting typos, formatting and references.  ... 
doi:10.1098/rsif.2017.0387 pmid:29618526 pmcid:PMC5938574 fatcat:65o4xmp53nc6zmj37srzuht6tq

Artificial Intellgence – Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021 [article]

Karl-Herbert Schäfer
2021 arXiv   pre-print
The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.  ...  and management) and the University of Applied Sciences and Arts Northwestern Switzerland.  ...  Acknowledgements The authors would like to thank D. Iordanov for the contribution to the development of the application within his studies at Mainz University of Applied Sciences.  ... 
arXiv:2112.05657v1 fatcat:wdjgymicyrfybg5zth2dc2i3ni

CARS 2021: Computer Assisted Radiology and Surgery Proceedings of the 35th International Congress and Exhibition Munich, Germany, June 21–25, 2021

2021 International Journal of Computer Assisted Radiology and Surgery  
The University Rovira i Virgili also supports this work with project 2019PFR-B2-61. References  ...  The algorithm has been tested on several CE-NBI datasets for the classification of laryngeal lesions and histopathologies.  ...  References For daily care, lack of both surgical resources and resilient infrastructure (e.g. power outages) are frequent contributors to avoidable morbidity/mortality.  ... 
doi:10.1007/s11548-021-02375-4 pmid:34085172 fatcat:6d564hsv2fbybkhw4wvc7uuxcy

Deep Learning for Histopathology Image Analysis From Heterogeneous and Multimodal Data Sources

Juan Sebastian Otalora Montenegro, Henning Muller, Stéphane Marchand-Maillet
Thank you, Anjani and Mina, for preparing such a lovely gift!  ...  Thank you to all the interns and temporal team members; I also share some incredible memories with them: Giovanni, Alperen, Stefano, Dimitry, Giulio, Liviu, Amjad (now colleague at Inselspital!)  ...  Semi-supervised learning: Semi-supervised learning can be defined as the learning paradigm that follows the set of machine learning models that are between unsupervised (training with datasets that do  ... 
doi:10.13097/archive-ouverte/unige:160358 fatcat:o7pm7mj3uzb7bezjkqrwyy3pnm

A two-stage heuristic for the university course timetabling problem [chapter]

Máté Pintér, Balázs Dávid
2019 StuCoSReC. Proceedings of the 2019 6th Student Computer Science Research Conference  
Published by University of Primorska Press Titov trg 4, si-6000 Koper Editor-in-Chief Jonatan Vinkler Managing Editor Alen Ježovnik Koper, 2019 isBN 978-961-7055-82-5 (pdf)  ...  All these factors influence the quality of the data and the later classification with machine learning.  ...  It is a method of unsupervised machine learning for pattern recognition, data mining, supervised machine learning, image analysis, bioinformatics, prediction, etc.  ... 
doi:10.26493/978-961-7055-82-5.27-30 fatcat:mv36atnxqvczjg7m7aetrpvy6y

2021 AIUM Award Winners

2021 Journal of ultrasound in medicine  
A S82 Moreno M S102 Morgan T S68, S107 Morris R S65 Muhtadi S S5 Munjal H S97, S104 Murrett J S74, S77 Mutambuze J S44, S45, S47 N Nagarajan E S34, S35 Nagdev A S145 Naief A S16 Narayanamoorthy S S65  ...  S133 McWhirter A S123 Mehta-Lee S S55, S64 Melniker L S71 Mendez A S105 Mendez K S74 Mengsteab P S132 Messina M S33 Meteer S S138 Meyer M S54, S63 Michael S S139 Michel B S167 Miller D S11 Miller H S125  ...  We designed an Atheromatic 2.0 system consisting of 3 kinds of deep learning (DL) classification paradigms: Deep Convolutional Neural Network (DCNN), Visual Geometric Group-16 (VGG16), and transfer learning  ... 
doi:10.1002/jum.15752 fatcat:v4nx5fvjwndrzfppaiaylgon64

ECR 2015 Book of Abstracts - A - Postgraduate Educational Programme

2015 Insights into Imaging  
Therefore, the teacher acts as a knowledge dispenser for passive students.  ...  Learning Objectives: 1. To learn how to cope with the overwhelming amount of available information. 2. To become familiar with resources adequate for trainees. 3.  ...  Learning Objectives: 1. To appreciate the potential of histopathology. 2. To appreciate the limitations of histopathology. 3. To learn about the role of novel molecular markers.  ... 
doi:10.1007/s13244-015-0386-0 pmid:25708993 pmcid:PMC4349897 fatcat:m7eyvqcwojfpvf3lr5hy6dwjb4

ECR 2016 Book of Abstracts - A - Postgraduate Educational Programme

2016 Insights into Imaging  
Therefore, the teacher acts as a knowledge dispenser for passive students.  ...  Learning Objectives: 1. To learn how to cope with the overwhelming amount of available information. 2. To become familiar with resources adequate for trainees. 3.  ...  Learning Objectives: 1. To appreciate the potential of histopathology. 2. To appreciate the limitations of histopathology. 3. To learn about the role of novel molecular markers.  ... 
doi:10.1007/s13244-016-0474-9 pmid:26873353 pmcid:PMC4762839 fatcat:itxslbcacjhh3kixfkcwmdbt44
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