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For: Piccialli F, Somma VD, Giampaolo F, Cuomo S, Fortino G. A survey on deep learning in medicine: Why, how and when? Information Fusion 2021;66:111-37. [DOI: 10.1016/j.inffus.2020.09.006] [Cited by in Crossref: 26] [Cited by in F6Publishing: 4] [Article Influence: 26.0] [Reference Citation Analysis]
Number Citing Articles
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4 Deperlioglu O, Kose U, Gupta D, Khanna A, Giampaolo F, Fortino G. Explainable framework for Glaucoma diagnosis by image processing and convolutional neural network synergy: Analysis with doctor evaluation. Future Generation Computer Systems 2022;129:152-69. [DOI: 10.1016/j.future.2021.11.018] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
5 Blanes-Selva V, Doñate-Martínez A, Linklater G, García-Gómez JM. Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs. Health Informatics J 2022;28:14604582221092592. [PMID: 35642719 DOI: 10.1177/14604582221092592] [Reference Citation Analysis]
6 Henkes A, Wessels H, Mahnken R. Physics informed neural networks for continuum micromechanics. Computer Methods in Applied Mechanics and Engineering 2022;393:114790. [DOI: 10.1016/j.cma.2022.114790] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
7 Aljabri M, Alghamdi M. A review on the use of deep learning for medical images segmentation. Neurocomputing 2022;506:311-35. [DOI: 10.1016/j.neucom.2022.07.070] [Reference Citation Analysis]
8 Zollner FG, Kocinski M, Hansen L, Golla A, Trbalic AS, Lundervold A, Materka A, Rogelj P. Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and Prospects. IEEE Access 2021;9:71577-605. [DOI: 10.1109/access.2021.3078430] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 5.0] [Reference Citation Analysis]
9 Piccialli F, Giampaolo F, Prezioso E, Camacho D, Acampora G. Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion. Information Fusion 2021;74:1-16. [DOI: 10.1016/j.inffus.2021.03.004] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
10 Yuan J, Ran X, Liu K, Yao C, Yao Y, Wu H, Liu Q. Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review. J Neurosci Methods 2021;368:109441. [PMID: 34942271 DOI: 10.1016/j.jneumeth.2021.109441] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
11 Mohammad-Rahimi H, Motamedian SR, Pirayesh Z, Haiat A, Zahedrozegar S, Mahmoudinia E, Rohban MH, Krois J, Lee JH, Schwendicke F. Deep learning in periodontology and oral implantology: A scoping review. J Periodontal Res 2022. [PMID: 35856183 DOI: 10.1111/jre.13037] [Reference Citation Analysis]
12 Leibetseder A, Schoeffmann K, Keckstein J, Keckstein S. Endometriosis detection and localization in laparoscopic gynecology. Multimed Tools Appl 2022;81:6191-215. [DOI: 10.1007/s11042-021-11730-1] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Hervella ÁS, Rouco J, Novo J, Ortega M. Retinal microaneurysms detection using adversarial pre-training with unlabeled multimodal images. Information Fusion 2022;79:146-61. [DOI: 10.1016/j.inffus.2021.10.003] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
14 Wang J, Jia Y, Wang D, Xiao W, Wang Z. Weighted IForest and Siamese GRU on Small Sample Anomaly Detection in Healthcare. Computer Methods and Programs in Biomedicine 2022. [DOI: 10.1016/j.cmpb.2022.106706] [Reference Citation Analysis]
15 Yin P, Cai H, Wu Q. DF-Net: Deep fusion network for multi-source vessel segmentation. Information Fusion 2022;78:199-208. [DOI: 10.1016/j.inffus.2021.09.010] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Cui J, Wang L, He X, De Albuquerque VHC, Alqahtani SA, Hassan MM. Deep learning-based multidimensional feature fusion for classification of ECG arrhythmia. Neural Comput & Applic. [DOI: 10.1007/s00521-021-06487-5] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Jin ZC, Zhong BY. Application of radiomics in hepatocellular carcinoma: A review. Artif Intell Med Imaging 2021; 2(3): 64-72 [DOI: 10.35711/aimi.v2.i3.64] [Reference Citation Analysis]
18 Gopukumar D, Ghoshal A, Zhao H. A Machine Learning Approach for Predicting Readmission Charges Billed by Hospitals. JMIR Med Inform 2022. [PMID: 35896038 DOI: 10.2196/37578] [Reference Citation Analysis]
19 Ali K, Shaikh ZA, Khan AA, Laghari AA. Multiclass skin cancer classification using EfficientNets – a first step towards preventing skin cancer. Neuroscience Informatics 2022;2:100034. [DOI: 10.1016/j.neuri.2021.100034] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
20 Greco A, Percannella G, Ritrovato P, Saggese A, Vento M. A deep learning based system for handwashing procedure evaluation. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07194-5] [Reference Citation Analysis]
21 Ertuğrul ÖF, Akıl MF. Detecting hemorrhage types and bounding box of hemorrhage by deep learning. Biomedical Signal Processing and Control 2022;71:103085. [DOI: 10.1016/j.bspc.2021.103085] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Romero-zaliz R, Pérez E, Jiménez-molinos F, Wenger C, Roldán JB. Study of Quantized Hardware Deep Neural Networks Based on Resistive Switching Devices, Conventional versus Convolutional Approaches. Electronics 2021;10:346. [DOI: 10.3390/electronics10030346] [Cited by in Crossref: 12] [Cited by in F6Publishing: 5] [Article Influence: 12.0] [Reference Citation Analysis]
23 Wang K, Zhang X, Lu Y, Zhang X, Zhang W. CGRNet: Contour-guided graph reasoning network for ambiguous biomedical image segmentation. Biomedical Signal Processing and Control 2022;75:103621. [DOI: 10.1016/j.bspc.2022.103621] [Reference Citation Analysis]
24 Naveiro JM, Puértolas S, Rosell J, Hidalgo A, Ibarz E, Albareda J, Gracia L. A new approach for initial callus growth during fracture healing in long bones. Comput Methods Programs Biomed 2021;208:106262. [PMID: 34260972 DOI: 10.1016/j.cmpb.2021.106262] [Reference Citation Analysis]
25 Qureshi MA, Qureshi KN, Jeon G, Piccialli F. Deep learning-based ambient assisted living for self-management of cardiovascular conditions. Neural Comput & Applic. [DOI: 10.1007/s00521-020-05678-w] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 5.0] [Reference Citation Analysis]
26 Anaya-isaza A, Mera-jimenez L. Data Augmentation and Transfer Learning for Brain Tumor Detection in Magnetic Resonance Imaging. IEEE Access 2022;10:23217-33. [DOI: 10.1109/access.2022.3154061] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
27 Wong KKL, Zhang A, Yang K, Wu S, Ghista DN. GCW-UNet segmentation of cardiac magnetic resonance images for evaluation of left atrial enlargement. Comput Methods Programs Biomed 2022;221:106915. [PMID: 35653942 DOI: 10.1016/j.cmpb.2022.106915] [Reference Citation Analysis]
28 Macias E, Lopez Vicario J, Serrano J, Ibeas J, Morell A. Transfer Learning Improving Predictive Mortality Models for Patients in End-Stage Renal Disease. Electronics 2022;11:1447. [DOI: 10.3390/electronics11091447] [Reference Citation Analysis]
29 Luca AR, Ursuleanu TF, Gheorghe L, Grigorovici R, Iancu S, Hlusneac M, Grigorovici A. Impact of quality, type and volume of data used by deep learning models in the analysis of medical images. Informatics in Medicine Unlocked 2022;29:100911. [DOI: 10.1016/j.imu.2022.100911] [Reference Citation Analysis]
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31 Groh R, Lei Z, Martignetti L, Li-jessen NYK, Kist AM. Efficient and Explainable Deep Neural Networks for Airway Symptom Detection in Support of Wearable Health Technology. Advanced Intelligent Systems. [DOI: 10.1002/aisy.202100284] [Reference Citation Analysis]
32 de Souza Brito A, Vieira MB, de Andrade MLSC, Feitosa RQ, Giraldi GA. Combining max-pooling and wavelet pooling strategies for semantic image segmentation. Expert Systems with Applications 2021;183:115403. [DOI: 10.1016/j.eswa.2021.115403] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
33 Ma Z, Mei G, Prezioso E, Zhang Z, Xu N. A deep learning approach using graph convolutional networks for slope deformation prediction based on time-series displacement data. Neural Comput & Applic 2021;33:14441-57. [DOI: 10.1007/s00521-021-06084-6] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 5.0] [Reference Citation Analysis]
34 Zhao L, Li K, Pu B, Chen J, Li S, Liao X. An ultrasound standard plane detection model of fetal head based on multi-task learning and hybrid knowledge graph. Future Generation Computer Systems 2022. [DOI: 10.1016/j.future.2022.04.011] [Reference Citation Analysis]
35 Zhou X, Ye Q, Yang X, Chen J, Ma H, Xia J, Del Ser J, Yang G. AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07048-0] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
36 Milosevic D, Vodanovic M, Galic I, Subasic M. A Comprehensive Exploration of Neural Networks for Forensic Analysis of Adult Single Tooth X-Ray Images. IEEE Access 2022;10:70980-1002. [DOI: 10.1109/access.2022.3187959] [Reference Citation Analysis]
37 Gumaei A, Ismail WN, Rafiul Hassan M, Hassan MM, Mohamed E, Alelaiwi A, Fortino G. A Decision-Level Fusion Method for COVID-19 Patient Health Prediction. Big Data Research 2022;27:100287. [DOI: 10.1016/j.bdr.2021.100287] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
38 Xu F, Guo G, Zhu F, Tan X, Fan L. Protein deep profile and model predictions for identifying the causal genes of male infertility based on deep learning. Information Fusion 2021;75:70-89. [DOI: 10.1016/j.inffus.2021.04.012] [Reference Citation Analysis]
39 Jang J, Lee HH, Park JA, Kim H. Unsupervised anomaly detection using generative adversarial networks in 1H-MRS of the brain. J Magn Reson 2021;325:106936. [PMID: 33639596 DOI: 10.1016/j.jmr.2021.106936] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
40 Ahmed I, Jeon G, Piccialli F. A Deep-Learning-Based Smart Healthcare System for Patient’s Discomfort Detection at the Edge of Internet of Things. IEEE Internet Things J 2021;8:10318-26. [DOI: 10.1109/jiot.2021.3052067] [Cited by in Crossref: 11] [Cited by in F6Publishing: 4] [Article Influence: 11.0] [Reference Citation Analysis]
41 Montalbo FJP. Diagnosing gastrointestinal diseases from endoscopy images through a multi-fused CNN with auxiliary layers, alpha dropouts, and a fusion residual block. Biomedical Signal Processing and Control 2022;76:103683. [DOI: 10.1016/j.bspc.2022.103683] [Reference Citation Analysis]
42 Wen J, Yan T, Su Z, Huang H, Gao Q, Chen X, Wong KK, Peng L. Risk Evaluation of Type B Aortic Dissection Based on WSS-based Indicators Distribution in Different Types of Aortic Arch. Computer Methods and Programs in Biomedicine 2022. [DOI: 10.1016/j.cmpb.2022.106872] [Reference Citation Analysis]