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Cited by in F6Publishing
For: Cai S, Tian Y, Lui H, Zeng H, Wu Y, Chen G. Dense-UNet: a novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network. Quant Imaging Med Surg 2020;10:1275-85. [PMID: 32550136 DOI: 10.21037/qims-19-1090] [Cited by in Crossref: 6] [Cited by in F6Publishing: 24] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Yu H, Sharifai N, Jiang K, Wang F, Teodoro G, Farris AB, Kong J. Artificial intelligence based liver portal tract region identification and quantification with transplant biopsy whole-slide images. Comput Biol Med 2022;150:106089. [PMID: 36137315 DOI: 10.1016/j.compbiomed.2022.106089] [Reference Citation Analysis]
2 Kugelman J, Allman J, Read SA, Vincent SJ, Tong J, Kalloniatis M, Chen FK, Collins MJ, Alonso-Caneiro D. A comparison of deep learning U-Net architectures for posterior segment OCT retinal layer segmentation. Sci Rep 2022;12:14888. [PMID: 36050364 DOI: 10.1038/s41598-022-18646-2] [Reference Citation Analysis]
3 Li W, Tang YM, Wang Z, Yu KM, To S. Atrous residual interconnected encoder to attention decoder framework for vertebrae segmentation via 3D volumetric CT images. Engineering Applications of Artificial Intelligence 2022;114:105102. [DOI: 10.1016/j.engappai.2022.105102] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Rajendran P, Pramanik M. High frame rate (∼3 Hz) circular photoacoustic tomography using single-element ultrasound transducer aided with deep learning. J Biomed Opt 2022;27. [DOI: 10.1117/1.jbo.27.6.066005] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Yang L, Gao S, Li P, Shi J, Zhou F. Recognition and Segmentation of Individual Bone Fragments with a Deep Learning Approach in CT Scans of Complex Intertrochanteric Fractures: A Retrospective Study. J Digit Imaging 2022. [PMID: 35711073 DOI: 10.1007/s10278-022-00669-w] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Mishra AK, Roy P, Bandyopadhyay S, Das SK. A multi‐task learning based approach for efficient breast cancer detection and classification. Expert Systems. [DOI: 10.1111/exsy.13047] [Reference Citation Analysis]
7 [DOI: 10.1109/icassp43922.2022.9747266] [Reference Citation Analysis]
8 Sun M, Wang Y, Fu Z, Li L, Liu Y, Zhao X. A Machine Learning Method for Automated In Vivo Transparent Vessel Segmentation and Identification Based on Blood Flow Characteristics. Microsc Microanal 2022;:1-14. [PMID: 35387704 DOI: 10.1017/S1431927622000514] [Reference Citation Analysis]
9 Cai S, Wu Y, Chen G. A Novel Elastomeric UNet for Medical Image Segmentation. Front Aging Neurosci 2022;14:841297. [DOI: 10.3389/fnagi.2022.841297] [Reference Citation Analysis]
10 Ye G, Kaya M. Automated Cell Foreground–Background Segmentation with Phase-Contrast Microscopy Images: An Alternative to Machine Learning Segmentation Methods with Small-Scale Data. Bioengineering 2022;9:81. [DOI: 10.3390/bioengineering9020081] [Reference Citation Analysis]
11 Sarti M, Parlani M, Diaz-gomez L, Mikos AG, Cerveri P, Casarin S, Dondossola E. Deep Learning for Automated Analysis of Cellular and Extracellular Components of the Foreign Body Response in Multiphoton Microscopy Images. Front Bioeng Biotechnol 2022;9:797555. [DOI: 10.3389/fbioe.2021.797555] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Dao T, Pham Q, Hwang W. FastMDE: A Fast CNN Architecture for Monocular Depth Estimation at High Resolution. IEEE Access 2022;10:16111-22. [DOI: 10.1109/access.2022.3145969] [Reference Citation Analysis]
13 Solovyev R, Kalinin AA, Gabruseva T. 3D convolutional neural networks for stalled brain capillary detection. Comput Biol Med 2021;141:105089. [PMID: 34920160 DOI: 10.1016/j.compbiomed.2021.105089] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
14 Xu S, Zhang Z, Zhou Q, Shao W, Tan W. A Pulmonary Vascular Extraction Algorithm from Chest CT/CTA Images. J Healthc Eng 2021;2021:5763177. [PMID: 34777735 DOI: 10.1155/2021/5763177] [Reference Citation Analysis]
15 Luximon DC, Abdulkadir Y, Chow PE, Morris ED, Lamb JM. Machine-assisted interpolation algorithm for semi-automated segmentation of highly deformable organs. Med Phys 2021. [PMID: 34783027 DOI: 10.1002/mp.15351] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
16 Rosas-Gonzalez S, Birgui-Sekou T, Hidane M, Zemmoura I, Tauber C. Asymmetric Ensemble of Asymmetric U-Net Models for Brain Tumor Segmentation With Uncertainty Estimation. Front Neurol 2021;12:609646. [PMID: 34659077 DOI: 10.3389/fneur.2021.609646] [Reference Citation Analysis]
17 Rajendran P, Pramanik M. Deep-learning-based multi-transducer photoacoustic tomography imaging without radius calibration. Opt Lett 2021;46:4510-3. [PMID: 34525034 DOI: 10.1364/OL.434513] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
18 Lin Y, Lin H, Zhu X, Chen G. Three-dimensional characterizations of two-photon excitation fluorescence images of elastic fibers affected by cutaneous scar duration. Quant Imaging Med Surg 2021;11:3584-94. [PMID: 34341733 DOI: 10.21037/qims-20-1051] [Reference Citation Analysis]
19 Munusamy H, Karthikeyan JM, Shriram G, Thanga Revathi S, Aravindkumar S. FractalCovNet architecture for COVID-19 Chest X-ray image Classification and CT-scan image Segmentation. Biocybern Biomed Eng 2021;41:1025-38. [PMID: 34257471 DOI: 10.1016/j.bbe.2021.06.011] [Cited by in F6Publishing: 9] [Reference Citation Analysis]
20 Wang Y, Zhang Y, Wen Z, Tian B, Kao E, Liu X, Xuan W, Ordovas K, Saloner D, Liu J. Deep learning based fully automatic segmentation of the left ventricular endocardium and epicardium from cardiac cine MRI. Quant Imaging Med Surg 2021;11:1600-12. [PMID: 33816194 DOI: 10.21037/qims-20-169] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
21 Huang L, Li M, Gou S, Zhang X, Jiang K. Automated Segmentation Method for Low Field 3D Stomach MRI Using Transferred Learning Image Enhancement Network. Biomed Res Int. 2021;2021:6679603. [PMID: 33628806 DOI: 10.1155/2021/6679603] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
22 Xue H, Zhang Q, Zou S, Zhang W, Zhou C, Tie C, Wan Q, Teng Y, Li Y, Liang D, Liu X, Yang Y, Zheng H, Zhu X, Hu Z. LCPR-Net: low-count PET image reconstruction using the domain transform and cycle-consistent generative adversarial networks. Quant Imaging Med Surg 2021;11:749-62. [PMID: 33532274 DOI: 10.21037/qims-20-66] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
23 Shozu K, Komatsu M, Sakai A, Komatsu R, Dozen A, Machino H, Yasutomi S, Arakaki T, Asada K, Kaneko S, Matsuoka R, Nakashima A, Sekizawa A, Hamamoto R. Model-Agnostic Method for Thoracic Wall Segmentation in Fetal Ultrasound Videos. Biomolecules 2020;10:E1691. [PMID: 33348873 DOI: 10.3390/biom10121691] [Cited by in Crossref: 10] [Cited by in F6Publishing: 15] [Article Influence: 5.0] [Reference Citation Analysis]
24 Rajendran P, Pramanik M. Deep learning approach to improve tangential resolution in photoacoustic tomography. Biomed Opt Express 2020;11:7311-23. [PMID: 33408998 DOI: 10.1364/BOE.410145] [Cited by in Crossref: 6] [Cited by in F6Publishing: 9] [Article Influence: 3.0] [Reference Citation Analysis]
25 Schroeder AB, Dobson ETA, Rueden CT, Tomancak P, Jug F, Eliceiri KW. The ImageJ ecosystem: Open-source software for image visualization, processing, and analysis. Protein Sci 2021;30:234-49. [PMID: 33166005 DOI: 10.1002/pro.3993] [Cited by in Crossref: 11] [Cited by in F6Publishing: 20] [Article Influence: 5.5] [Reference Citation Analysis]