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Advances in Deep Learning-Based Medical Image Analysis
2021
Health Data Science
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This paper reviewed the advancement of convolutional neural network-based techniques in clinical applications. ...
With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active ...
AlexNet [25] was a pioneer convolutional neural network, which was composed of repeated convolutions, each followed by ReLU and max pooling operation with stride for downsampling. ...
doi:10.34133/2021/8786793
fatcat:d6nkb4yoxrcgni4y5owju5pnh4
Osteolysis: A Literature Review of Basic Science and Potential Computer-Based Image Processing Detection Methods
2021
Computational Intelligence and Neuroscience
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methods, especially with the use of artificial neural network algorithms. ...
Deep learning algorithms with a variety of neural network structures such as CNN, U-Net, and Seg-UNet have proved to be efficient algorithms for medical image processing specifically in the field of orthopedics ...
us, a semantic segmentation structure U-Net based on fully convolutional neural network can be an efficient method for the detection and classification of osteolytic legions. ...
doi:10.1155/2021/4196241
pmid:34646317
pmcid:PMC8505126
fatcat:inqln4wldrfwnlb2jgaobouk6m
A Novel Bio-Inspired Deep Learning Approach for Liver Cancer Diagnosis
2020
Information
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For assessing performance of the two proposed algorithms, comparisons have been made to the state-of-the-art algorithms on liver lesion segmentation and classification. ...
Secondly, a hybrid algorithm of the LeNet-5 model and ABC algorithm, namely, LeNet-5/ABC, is proposed as feature extractor and classifier of liver lesions. ...
Conflicts of Interest: The author declares no conflict of interest. ...
doi:10.3390/info11020080
fatcat:kfm7ewfplbacnfqqoliucua6by
Deep Fusion Models of Multi-phase CT and Selected Clinical Data for Preoperative Prediction of Early Recurrence in Hepatocellular Carcinoma
2020
IEEE Access
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Moreover, fusion models with a joint loss function can further improve the prediction performance to 80.49% and 0.8331. ...
In this paper, we proposed a deep-learning based prediction model to extract high-level features from the triple-phase CT images and compare its performance with traditional radiomics model and clinical ...
ACKNOWLEDGMENT (Weibin Wang and Qingqing Chen are co-first authors.) ...
doi:10.1109/access.2020.3011145
fatcat:vvw4rpo5ljagze4iyczvth6eda
2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24
2020
IEEE journal of biomedical and health informatics
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., and Inan, O.T., A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning; JBHI May 2020 1296-1309 Herskovic, V., see Saint-Pierre ...
2020 3529-3538 Honda, O., see Xu, R., 2041-2052 Hong, H., see 2833-2843 Hong, H., see Xue, B., JBHI Feb. 2020 614-625 Hoog Antink, C., Mai, Y., Aalto, R., Bruser, C., Leonhardt, S., Oksala, N., and ...
., +, JBHI Oct. 2020 2860-2869 GoogLeNet-Based Ensemble FCNet Classifier for Focal Liver Lesion Diagnosis. ...
doi:10.1109/jbhi.2020.3048808
fatcat:iifrkwtzazdmboabdqii7x5ukm
Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
2019
Journal of digital imaging
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It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. ...
In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. ...
Approaches/Network Structures
Convolutional Neural Networks (CNNs) A CNN is a branch of neural networks and consists of a stack of layers each performing a specific operation, e.g., convolution, pooling ...
doi:10.1007/s10278-019-00227-x
pmid:31144149
pmcid:PMC6646484
fatcat:bupmhghuxvgthfo2fl7bkkry4a
Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions
2021
Computational and Mathematical Methods in Medicine
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Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. ...
DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. ...
The authors in [67] come up with different variants of fully convolutional networks (FCNs) for the segmentation of lesions of breast cancer subjects. ...
doi:10.1155/2021/9025470
pmid:34754327
pmcid:PMC8572604
fatcat:wgpostjgsfeijazpyguobcrx4i
Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art
2022
Journal of Imaging
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Deep learning (DL), particularly convolutional neural networks, has produced outstanding success in classifying and segmenting images. ...
Some difficulties with manual segmentation have necessitated the use of deep learning models to assist clinicians in effectively recognizing and segmenting tumors. ...
Concatenation of local pathways is another variant of cascaded design [90] . ...
doi:10.3390/jimaging8030055
pmid:35324610
pmcid:PMC8954467
fatcat:7dhh3zwk5zcmpe3ijzbgpmo4ze
Learning Neural Textual Representations for Citation Recommendation
2021
2020 25th International Conference on Pattern Recognition (ICPR)
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; Skocaj, Danijel 1532 End-To-End Training of a Two-Stage Neural Network for Defect Detection GAP: Quantifying the Generative Adversarial Set and Class Feature Applicability of Deep Neural Networks Deep ...
Narasimha
2233
Region and Relations Based Multi Attention Network for Graph
Classification
DAY 1 -Jan 12, 2021
Zhou, Shibo; Li, Xiaohua
2242
Spiking Neural Networks with Single-Spike Temporal-Coded ...
doi:10.1109/icpr48806.2021.9412725
fatcat:3vge2tpd2zf7jcv5btcixnaikm
Cerebral Small Vessel Disease: A Review Focusing on Pathophysiology, Biomarkers, and Machine Learning Strategies
2018
Journal of Stroke
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Application of neural networking, machine learning and deep learning in image processing have increased significantly for correct severity of cSVD. ...
It also focuses on the role of deep machine strategies and other dimensions of cSVD by linking it with several cerebral and non-cerebral diseases as well as recent advances in the field to achieve sensitive ...
Figure 4 . 4 Architecture of typical Convolutional Neural Networks (CNN) for image processing. ...
doi:10.5853/jos.2017.02922
pmid:30309226
pmcid:PMC6186915
fatcat:znrvqbctozc7hm2r3qrmcthqc4
Deep Learning in Cardiology
2019
IEEE Reviews in Biomedical Engineering
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2019 pre-print arXiv:1902.11122v1 fatcat:xeollgq57fb3zkwmxnm5uwo7kmOther Versions
We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use. ...
The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. ...
[199] trained a seven layer CNN using patches for the segmentation and classification of coronary lesions in CT images. ...
doi:10.1109/rbme.2018.2885714
fatcat:pa47trmskvflvig5cotth265q4
Cytology Image Analysis Techniques Towards Automation: Systematically Revisited
[article]
2020
arXiv
pre-print
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Cytology is the branch of pathology which deals with the microscopic examination of cells for diagnosis of carcinoma or inflammatory conditions. ...
We take a short tour to 17 types of cytology and explore various segmentation and/or classification techniques which evolved during last three decades boosting the concept of automation in cytology. ...
Authors are also thankful to the members of "Theism Medical Diagnostics Centre", Kolkata, India and "Saroj Gupta Cancer Centre & Research Institute", Thakurpukur, Kolkata, India. ...
arXiv:2003.07529v1
fatcat:eossjujftzflbfnfhbsw55tlta
Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine
2021
Biomedicines
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Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver with high morbidity and mortality rates worldwide. ...
Our review focuses on the recent progress in integrative proteomic profiling strategies and their usage in combination with machine learning and deep learning technologies for the discovery of novel biomarker ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/biomedicines9020159
pmid:33562077
fatcat:nflz35zpczdadji5w4d3bhtf3q
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
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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 ...
the exchange/communication of R&D ideas by means of verbal and written statements made by responsible authors, scrutinized by informed reviewers and utilized by an open-minded audience, with the aim to ...
Besides, use of gloves is low, so doses of hands are still high. Therefore, a master-slave robotic system for VI is necessary for minimization of the radiation exposure. ...
doi:10.1007/s11548-020-02171-6
pmid:32514840
fatcat:lyhdb2zfpjcqbf4mmbunddwroq
ESGAR 2019 Book of Abstracts
2019
Insights into Imaging
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We developed a fully automated convolutional neural network (CNN)-based method to generate ROIs on liver MRE images, and examined agreement in liver stiffness estimation between automated and manual ROI ...
Advanced MRI in liver imaging SS 12.1 Agreement between MR elastography liver stiffness estimates obtained from fully automated convolutional neural network-based and manually drawn regions-ofinterest ...
doi:10.1186/s13244-019-0748-0
pmid:31165358
pmcid:PMC6548780
fatcat:te5vgmwwlzcw5ou5wz3vdpsoiu
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