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For: Jansen-Winkeln B, Barberio M, Chalopin C, Schierle K, Diana M, Köhler H, Gockel I, Maktabi M. Feedforward Artificial Neural Network-Based Colorectal Cancer Detection Using Hyperspectral Imaging: A Step towards Automatic Optical Biopsy. Cancers (Basel) 2021;13:967. [PMID: 33669082 DOI: 10.3390/cancers13050967] [Cited by in Crossref: 24] [Cited by in F6Publishing: 24] [Article Influence: 12.0] [Reference Citation Analysis]
Number Citing Articles
1 Muniz FB, Baffa MDFO, Garcia SB, Bachmann L, Felipe JC. Histopathological Diagnosis of Colon Cancer Using Micro-FTIR Hyperspectral Imaging and Deep Learning. Computer Methods and Programs in Biomedicine 2023. [DOI: 10.1016/j.cmpb.2023.107388] [Reference Citation Analysis]
2 Avram MF, Lazăr DC, Mariş MI, Olariu S. Artificial intelligence in improving the outcome of surgical treatment in colorectal cancer. Front Oncol 2023;13:1116761. [PMID: 36733307 DOI: 10.3389/fonc.2023.1116761] [Reference Citation Analysis]
3 Pathak P, Chalopin C, Zick L, Köhler H, Pfahl A, Rayes N, Gockel I, Neumuth T, Melzer A, Jansen-Winkeln B, Maktabi M. Spectral Similarity Measures for In Vivo Human Tissue Discrimination Based on Hyperspectral Imaging. Diagnostics (Basel) 2023;13. [PMID: 36673005 DOI: 10.3390/diagnostics13020195] [Reference Citation Analysis]
4 Patrascu S, Cotofana-Graure GM, Surlin V, Mitroi G, Serbanescu MS, Geormaneanu C, Rotaru I, Patrascu AM, Ionascu CM, Cazacu S, Strambu VDE, Petru R. Preoperative Immunocite-Derived Ratios Predict Surgical Complications Better when Artificial Neural Networks Are Used for Analysis-A Pilot Comparative Study. J Pers Med 2023;13. [PMID: 36675762 DOI: 10.3390/jpm13010101] [Reference Citation Analysis]
5 Wang W, Zhang X, Wang SH, Zhang YD. Covid-19 Diagnosis by WE-SAJ. Syst Sci Control Eng 2022;10:325-35. [PMID: 36568847 DOI: 10.1080/21642583.2022.2045645] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
6 Cui R, Yu H, Xu T, Xing X, Cao X, Yan K, Chen J. Deep Learning in Medical Hyperspectral Images: A Review. Sensors (Basel) 2022;22. [PMID: 36560157 DOI: 10.3390/s22249790] [Reference Citation Analysis]
7 Hren R, Sersa G, Simoncic U, Milanic M. Imaging perfusion changes in oncological clinical applications by hyperspectral imaging: a literature review. Radiol Oncol 2022;56:420-9. [PMID: 36503709 DOI: 10.2478/raon-2022-0051] [Reference Citation Analysis]
8 Tharwat M, Sakr NA, El-Sappagh S, Soliman H, Kwak KS, Elmogy M. Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques. Sensors (Basel) 2022;22. [PMID: 36501951 DOI: 10.3390/s22239250] [Reference Citation Analysis]
9 Premachandran S, Haldavnekar R, Das S, Venkatakrishnan K, Tan B. DEEP Surveillance of Brain Cancer Using Self-Functionalized 3D Nanoprobes for Noninvasive Liquid Biopsy. ACS Nano 2022;16:17948-64. [PMID: 36112671 DOI: 10.1021/acsnano.2c04187] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
10 Martinez-Vega B, Tkachenko M, Matkabi M, Ortega S, Fabelo H, Balea-Fernandez F, La Salvia M, Torti E, Leporati F, Callico GM, Chalopin C. Evaluation of Preprocessing Methods on Independent Medical Hyperspectral Databases to Improve Analysis. Sensors (Basel) 2022;22. [PMID: 36433516 DOI: 10.3390/s22228917] [Reference Citation Analysis]
11 Collins T, Bencteux V, Benedicenti S, Moretti V, Mita MT, Barbieri V, Rubichi F, Altamura A, Giaracuni G, Marescaux J, Hostettler A, Diana M, Viola MG, Barberio M. Automatic optical biopsy for colorectal cancer using hyperspectral imaging and artificial neural networks. Surg Endosc 2022;36:8549-59. [PMID: 36008640 DOI: 10.1007/s00464-022-09524-z] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Zhang L, Huang D, Chen X, Zhu L, Chen X, Xie Z, Huang G, Gao J, Shi W, Cui G. Visible near-infrared hyperspectral imaging and supervised classification for the detection of small intestinal necrosis tissue in vivo. Biomed Opt Express 2022;13:6061-80. [PMID: 36733734 DOI: 10.1364/BOE.470202] [Reference Citation Analysis]
13 Okamoto N, Rodríguez-luna MR, Bencteux V, Al-taher M, Cinelli L, Felli E, Urade T, Nkusi R, Mutter D, Marescaux J, Hostettler A, Collins T, Diana M. Computer-Assisted Differentiation between Colon-Mesocolon and Retroperitoneum Using Hyperspectral Imaging (HSI) Technology. Diagnostics 2022;12:2225. [DOI: 10.3390/diagnostics12092225] [Reference Citation Analysis]
14 Chalopin C, Nickel F, Pfahl A, Köhler H, Maktabi M, Thieme R, Sucher R, Jansen-Winkeln B, Studier-Fischer A, Seidlitz S, Maier-Hein L, Neumuth T, Melzer A, Müller-Stich BP, Gockel I. [Artificial intelligence and hyperspectral imaging for image-guided assistance in minimally invasive surgery]. Chirurgie (Heidelb) 2022. [PMID: 35798904 DOI: 10.1007/s00104-022-01677-w] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Cihan M, Ceylan M, Ornek AH. Spectral-spatial classification for non-invasive health status detection of neonates using hyperspectral imaging and deep convolutional neural networks. Spectroscopy Letters. [DOI: 10.1080/00387010.2022.2076698] [Reference Citation Analysis]
16 Ghosh NK, Kumar A. Colorectal cancer: Artificial intelligence and its role in surgical decision making. Artif Intell Gastroenterol 2022; 3(2): 36-45 [DOI: 10.35712/aig.v3.i2.36] [Reference Citation Analysis]
17 Zenteno O, Trinh D, Treuillet S, Lucas Y, Bazin T, Lamarque D, Daul C. Optical biopsy mapping on endoscopic image mosaics with a marker-free probe. Computers in Biology and Medicine 2022;143:105234. [DOI: 10.1016/j.compbiomed.2022.105234] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Jansen-winkeln B, Köhler H, Pfahl A, Mehdorn M, Gockel I, Chalopin C, Maktabi M. Intraoperative Anwendung künstlicher Intelligenz und neuer hyperspektraler Bildgebungsverfahren in der kolorektalen Chirurgie. coloproctology. [DOI: 10.1007/s00053-022-00592-2] [Reference Citation Analysis]
19 Bale AS, Venugopal S, Gupta A, B PKS, R P, Paul JR, Sagar Naidu A. Cancer Detection using Artificial Neural Networking Techniques: A Study. 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) 2021. [DOI: 10.1109/icac3n53548.2021.9725641] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
20 Alameddine J, Chehdi K, Cariou C. Hierarchical Unsupervised Partitioning of Large Size Data and Its Application to Hyperspectral Images. Remote Sensing 2021;13:4874. [DOI: 10.3390/rs13234874] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
21 Barberio M, Benedicenti S, Pizzicannella M, Felli E, Collins T, Jansen-Winkeln B, Marescaux J, Viola MG, Diana M. Intraoperative Guidance Using Hyperspectral Imaging: A Review for Surgeons. Diagnostics (Basel) 2021;11:2066. [PMID: 34829413 DOI: 10.3390/diagnostics11112066] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
22 Maktabi M, Tkachenko M, Kohler H, Schierle K, Gockel I, Jansen-Winkeln B, Chalopin C. Using physiological parameters measured by hyperspectral imaging to detect colorectal cancer. Annu Int Conf IEEE Eng Med Biol Soc 2021;2021:3865-8. [PMID: 34892077 DOI: 10.1109/EMBC46164.2021.9630160] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
23 Zhuang H, Zhang J, Liao F. A systematic review on application of deep learning in digestive system image processing. Vis Comput 2021;:1-16. [PMID: 34744231 DOI: 10.1007/s00371-021-02322-z] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
24 Gockel I, Hartmann W, Köhler H, Leonhardi J, Heyn S, Thieme R. [Epigastric pain in "gastric tumors" : The hummingbird among the differential diagnoses]. Chirurg 2021. [PMID: 34665283 DOI: 10.1007/s00104-021-01522-6] [Reference Citation Analysis]
25 Collins T, Maktabi M, Barberio M, Bencteux V, Jansen-Winkeln B, Chalopin C, Marescaux J, Hostettler A, Diana M, Gockel I. Automatic Recognition of Colon and Esophagogastric Cancer with Machine Learning and Hyperspectral Imaging. Diagnostics (Basel) 2021;11:1810. [PMID: 34679508 DOI: 10.3390/diagnostics11101810] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
26 Barberio M, Collins T, Bencteux V, Nkusi R, Felli E, Viola MG, Marescaux J, Hostettler A, Diana M. Deep Learning Analysis of In Vivo Hyperspectral Images for Automated Intraoperative Nerve Detection. Diagnostics (Basel) 2021;11:1508. [PMID: 34441442 DOI: 10.3390/diagnostics11081508] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
27 Tu T, Wei X, Yang Y, Zhang N, Li W, Tu X, Li W. Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery. BMC Nephrol 2021;22:231. [PMID: 34147076 DOI: 10.1186/s12882-021-02421-y] [Reference Citation Analysis]
28 Jansen-winkeln B, Mehdorn M, Lange U, Köhler H, Chalopin C, Gockel I. Precision Surgery In Rectal Resection With Hyperspectral And Fluorescence Imaging And Pelvic Intraoperative Neuromonitoring (With Video). Surg Technol Int . [DOI: 10.52198/21.sti.38.cr1383] [Reference Citation Analysis]
29 Wang W. Covid-19 Detection by Wavelet Entropy and Jaya. Intelligent Computing Theories and Application 2021. [DOI: 10.1007/978-3-030-84532-2_45] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
30 Li Y, Huang H, Wu J, Tang Y. 3-D Convolutional Neural Network Driven by Dimensionality Reduction for Hyperspectral Blood Cells Classification. Lecture Notes in Computer Science 2021. [DOI: 10.1007/978-3-030-87358-5_59] [Reference Citation Analysis]