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Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations [article]

Hongwei Li, Fei-Fei Xue, Krishna Chaitanya, Shengda Luo, Ivan Ezhov, Benedikt Wiestler, Jianguo Zhang, Bjoern Menze
2021 arXiv   pre-print
We propose a self-supervised representation learning framework to learn high-level features of 3D volumes as a complement to existing radiomics features.  ...  Specifically, we demonstrate how to learn image representations in a self-supervised fashion using a 3D Siamese network.  ...  Our contribution is threefold: (1) We develop a 3D Siamese network to learn self-supervised representation which is high-level and discrminative. (2) For the first time, we explore how to tackle data imbalance  ... 
arXiv:2103.04167v2 fatcat:gd4fo5dirvff3mcyl2rq7fpaae

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
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  ...  Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging  ...  Self-supervised pretext tasks: Since self-supervision via pretext tasks and contrastive learning can learn rich semantic representations from unlabeled datasets, self-supervised learning is often used  ... 
arXiv:2105.13381v2 fatcat:2k342a6rhjaavpoa2qoqxhg5rq

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Xingping Zhang, Yanchun Zhang, Guijuan Zhang, Xingting Qiu, Wenjun Tan, Xiaoxia Yin, Liefa Liao
2022 Frontiers in Oncology  
In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation  ...  The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research  ...  Deep learning-based radiomics-driven multiclassification methods for breast cancer typically employ supervised models based on transfer learning (146) .  ... 
doi:10.3389/fonc.2022.773840 pmid:35251962 pmcid:PMC8891653 fatcat:3h5tnm3aznb33k5ylkcd6tvs4e

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions [article]

Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Diaz, Karim Lekadir
2021 arXiv   pre-print
The recent advancements in Generative Adversarial Networks (GANs) in computer vision as well as in medical imaging may provide a basis for enhanced capabilities in cancer detection and analysis.  ...  In this review, we assess the potential of GANs to address a number of key challenges of cancer imaging, including data scarcity and imbalance, domain and dataset shifts, data access and privacy, data  ...  Nie et al (2017) [111] context-aware GAN ADNI [170, 171] Cranial/pelvic MRI/CT Paired translation Supervised 3D GAN for MR-to-CT translation with 'Auto-Context Model' (ACM).  ... 
arXiv:2107.09543v1 fatcat:jz76zqklpvh67gmwnsdqzgq5he

Artificial Intelligence in Quantitative Ultrasound Imaging: A Review [article]

Boran Zhou, Xiaofeng Yang, Tian Liu
2020 arXiv   pre-print
Quantitative ultrasound (QUS) imaging is a reliable, fast and inexpensive technique to extract physically descriptive parameters for assessing pathologies.  ...  representation learning [31] .  ...  Deep learning (DL) is a subfield of representation learning where the learned features are compositional or hierarchical.  ... 
arXiv:2003.11658v1 fatcat:iujuh7gra5ax7od2gxoo6yrbpe

Artificial intelligence in cancer imaging: Clinical challenges and applications

Wenya Linda Bi, Ahmed Hosny, Matthew B. Schabath, Maryellen L. Giger, Nicolai J. Birkbak, Alireza Mehrtash, Tavis Allison, Omar Arnaout, Christopher Abbosh, Ian F. Dunn, Raymond H. Mak, Rulla M. Tamimi (+7 others)
2019 Ca  
Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability  ...  Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts  ...  Acknowledgments: We thank Ken Chang for generating the activation heatmaps in Figure 5 .  ... 
doi:10.3322/caac.21552 pmid:30720861 pmcid:PMC6403009 fatcat:czsiirkbm5f7fh2ueesgnqtlhi

AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

Hanqiu Deng, Xingyu Li
2021 Frontiers in Artificial Intelligence  
In this regard, we searched for papers and preprints on bioRxiv, medRxiv, and arXiv published for the period from January 1, 2020, to March 31, 2021, using the keywords of COVID, lung scans, and AI.  ...  The latest AI solutions to process and analyze chest images for COVID-19 treatment and their advantages and limitations are presented.  ...  It takes the numerical representations from pretrained ChXNet as input and innovates a non-iterative mapping for sparse representation learning.  ... 
doi:10.3389/frai.2021.612914 fatcat:lu7imttrkrhr7bes65p52dqr6i

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Muhammad Waqas Nadeem, Mohammed A. Al Ghamdi, Muzammil Hussain, Muhammad Adnan Khan, Khalid Masood Khan, Sultan H. Almotiri, Suhail Ashfaq Butt
2020 Brain Sciences  
A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area  ...  Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification  ...  Deep learning technologies in the medical field improve the awareness of bio mechanisms for brain tumor segmentation.  ... 
doi:10.3390/brainsci10020118 pmid:32098333 pmcid:PMC7071415 fatcat:wofq4puvcbemlconbz6carsf2y

Deep Semantic Segmentation of Natural and Medical Images: A Review [article]

Saeid Asgari Taghanaki, Kumar Abhishek, Joseph Paul Cohen, Julien Cohen-Adad, Ghassan Hamarneh
2020 arXiv   pre-print
sequenced models, weakly supervised, and multi-task methods and provide a comprehensive review of the contributions in each of these groups.  ...  Further, for each group, we analyze each variant of these groups and discuss the limitations of the current approaches and present potential future research directions for semantic image segmentation.  ...  discriminatory radiomic disease signatures.  ... 
arXiv:1910.07655v3 fatcat:uxrrmb3jofcsvnkfkuhfwi62yq

CARS 2020—Computer Assisted Radiology and Surgery Proceedings of the 34th International Congress and Exhibition, Munich, Germany, June 23–27, 2020

2020 International Journal of Computer Assisted Radiology and Surgery  
The traditional platforms of CARS Congresses for the scholarly publication and communication process for the presentation of R&D ideas were congress centers or hotels, typically hosting 600-800 participants  ...  Aiming to stimulate complimentary thoughts and actions on what is being presented at CARS, implies a number of enabling variables for optimal analogue scholarly communication, such as (examples given are  ...  Conclusion This work attempts to provide and validate a self-sustained tool for the automatic performance assessment of virtual temporal bone dissection performed within a mastoidectomy surgical simulator  ... 
doi:10.1007/s11548-020-02171-6 pmid:32514840 fatcat:lyhdb2zfpjcqbf4mmbunddwroq

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations [article]

Ayman Al-Kababji, Faycal Bensaali, Sarada Prasad Dakua, Yassine Himeur
2022 arXiv   pre-print
Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and they are further partitioned if the amount of work that falls under a certain scheme is significant  ...  Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing, such as the scarcity of many studies on the vessels' segmentation  ...  Simultaneously, to enhance the edge consistency of massive unlabeled patches, an edge-aware self-supervision architecture has been developed.  ... 
arXiv:2103.06384v2 fatcat:w6dxpyxhzzhs3gel25pgy6fqke

Deep Learning Based Brain Tumor Segmentation: A Survey [article]

Zhihua Liu, Lei Tong, Zheheng Jiang, Long Chen, Feixiang Zhou, Qianni Zhang, Xiangrong Zhang, Yaochu Jin, Huiyu Zhou
2021 arXiv   pre-print
We also provide insightful discussions for future development directions.  ...  A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results.  ...  There are clear region and label imbalances. Best viewed in colors. Fig. 4 . 4 A taxonomy of this survey for deep learning based brain tumor segmentation.  ... 
arXiv:2007.09479v3 fatcat:vdbpwfdsorfudkvnvottexd7je

Edge Constraint and Location Mapping for Liver Tumor Segmentation from Nonenhanced Images

Jina Zhang, Shichao Luo, Yan Qiang, Yuling Tian, Xiaojiao Xiao, Keqin Li, Xingxu Li, Po-Hsiang Tsui
2022 Computational and Mathematical Methods in Medicine  
We build the localization network, which generates prior coarse masks to provide position mapping for the segmentation network.  ...  To improve the ability of the model for capturing detailed features, sSE blocks and dense upward connections are introduced into it.  ...  We embed the sSE module in the network, which can automatically obtain the importance of each feature channel by self-learning.  ... 
doi:10.1155/2022/1248311 pmid:35309832 pmcid:PMC8926519 fatcat:g2yji2fiqfh7zmixnxl5kyj5qm

2021 Index IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 18

2022 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  Ge, Y., +, MRI Based Radiomics Approach With Deep Learning for Prediction of Vessel Invasion in Early-Stage Cervical Cancer.  ...  -Dec. 2021 2526-2534 MRI Based Radiomics Approach With Deep Learning for Prediction of Vessel Invasion in Early-Stage Cervical Cancer.  ... 
doi:10.1109/tcbb.2021.3136340 fatcat:bjvb334webfovh4nsc7oeds3di

Discriminating cognitive motor dissociation from disorders of consciousness using structural MRI

Polona Pozeg, Jane Jöhr, Alessandro Pincherle, Guillaume Marie, Philippe Ryvlin, Reto Meuli, Patric Hagmann, Karin Diserens, Vincent Dunet
2021 NeuroImage: Clinical  
Structural brain MRIs were qualitatively assessed for lesions in 18 brain regions.  ...  An accurate evaluation and detection of awareness after a severe brain injury is crucial to a patient's diagnosis, therapy, and end-of-life decisions.  ...  Melanie Price Hirt for proofreading this manuscript. This study was funded by the Swiss National Science Foundation [grant number: FNS 320030_189129].  ... 
doi:10.1016/j.nicl.2021.102651 pmid:33836454 fatcat:wdrltfs53zfpvffydneqzugi7q
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