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Advances in Deep Learning-Based Medical Image Analysis

Xiaoqing Liu, Kunlun Gao, Bo Liu, Chengwei Pan, Kongming Liang, Lifeng Yan, Jiechao Ma, Fujin He, Shu Zhang, Siyuan Pan, Yizhou Yu
2021 Health Data Science  
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

Soroush Baseri Saadi, Ramin Ranjbarzadeh, Ozeir kazemi, Amir Amirabadi, Saeid Jafarzadeh Ghoushchi, Oveis Kazemi, Sonya Azadikhah, Malika Bendechache, Navid Razmjooy
2021 Computational Intelligence and Neuroscience  
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  
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

Weibin Wang, Qingqing Chen, Yutaro Iwamoto, Panyanat Aonpong, Lanfen Lin, Hongjie Hu, Qiaowei Zhang, Yen-Wei Chen
2020 IEEE Access  
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  
., 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

Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He, Paul Kennedy
2019 Journal of digital imaging  
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

Ahsan Bin Tufail, Yong-Kui Ma, Mohammed K. A. Kaabar, Francisco Martínez, A. R. Junejo, Inam Ullah, Rahim Khan, Iman Yi Liao
2021 Computational and Mathematical Methods in Medicine  
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

Abubaker Abdelrahman, Serestina Viriri
2022 Journal of Imaging  
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

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
; 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

Elisa Cuadrado-Godia, Pratistha Dwivedi, Sanjiv Sharma, Angel Ois Santiago, Jaume Roquer Gonzalez, Mercedes Balcells, John Laird, Monika Turk, Harman S. Suri, Andrew Nicolaides, Luca Saba, Narendra N. Khanna (+1 others)
2018 Journal of Stroke  
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

Paschalis Bizopoulos, Dimitrios Koutsouris
2019 IEEE Reviews in Biomedical Engineering  
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]

Shyamali Mitra, Nibaran Das, Soumyajyoti Dey, Sukanta Chakrabarty, Mita Nasipuri, Mrinal Kanti Naskar
2020 arXiv   pre-print
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

Nurbubu T. Moldogazieva, Innokenty M. Mokhosoev, Sergey P. Zavadskiy, Alexander A. Terentiev
2021 Biomedicines  
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  
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  
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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