BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Thakur N, Yoon H, Chong Y. Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review. Cancers (Basel). 2020;12. [PMID: 32668721 DOI: 10.3390/cancers12071884] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 5.5] [Reference Citation Analysis]
Number Citing Articles
1 Yang H, Hu B. Early gastrointestinal cancer: The application of artificial intelligence. Artif Intell Gastrointest Endosc 2021; 2(4): 185-197 [DOI: 10.37126/aige.v2.i4.185] [Reference Citation Analysis]
2 Yoshida H, Kiyuna T. Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology. World J Gastroenterol 2021; 27(21): 2818-2833 [PMID: 34135556 DOI: 10.3748/wjg.v27.i21.2818] [Reference Citation Analysis]
3 Neto PC, Oliveira SP, Montezuma D, Fraga J, Monteiro A, Ribeiro L, Gonçalves S, Pinto IM, Cardoso JS. iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images. Cancers (Basel) 2022;14:2489. [PMID: 35626093 DOI: 10.3390/cancers14102489] [Reference Citation Analysis]
4 Pettit RW, Fullem R, Cheng C, Amos CI. Artificial intelligence, machine learning, and deep learning for clinical outcome prediction. Emerg Top Life Sci 2021:ETLS20210246. [PMID: 34927670 DOI: 10.1042/ETLS20210246] [Reference Citation Analysis]
5 Yao Y, Gou S, Tian R, Zhang X, He S. Automated Classification and Segmentation in Colorectal Images Based on Self-Paced Transfer Network. Biomed Res Int. 2021;2021:6683931. [PMID: 33542924 DOI: 10.1155/2021/6683931] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Qiu H, Ding S, Liu J, Wang L, Wang X. Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer. Current Oncology 2022;29:1773-95. [DOI: 10.3390/curroncol29030146] [Reference Citation Analysis]
7 Alloro R, Sinagra E. Artificial intelligence and colorectal cancer: How far can you go? Artif Intell Cancer 2021; 2(2): 7-11 [DOI: 10.35713/aic.v2.i2.7] [Reference Citation Analysis]
8 Chong Y, Thakur N, Lee JY, Hwang G, Choi M, Kim Y, Yu H, Cho MY. Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation. Diagn Pathol 2021;16:19. [PMID: 33706755 DOI: 10.1186/s13000-021-01081-8] [Reference Citation Analysis]
9 Ailia MJ, Thakur N, Abdul-ghafar J, Jung CK, Yim K, Chong Y. Current Trend of Artificial Intelligence Patents in Digital Pathology: A Systematic Evaluation of the Patent Landscape. Cancers 2022;14:2400. [DOI: 10.3390/cancers14102400] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Kim H, Yoon H, Thakur N, Hwang G, Lee EJ, Kim C, Chong Y. Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain. Sci Rep 2021;11:22520. [PMID: 34795365 DOI: 10.1038/s41598-021-01905-z] [Reference Citation Analysis]
11 Pritzker KPH. Colon Cancer Biomarkers: Implications for Personalized Medicine. J Pers Med. 2020;10. [PMID: 33066312 DOI: 10.3390/jpm10040167] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
12 Oliveira SP, Neto PC, Fraga J, Montezuma D, Monteiro A, Monteiro J, Ribeiro L, Gonçalves S, Pinto IM, Cardoso JS. CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance. Sci Rep 2021;11:14358. [PMID: 34257363 DOI: 10.1038/s41598-021-93746-z] [Reference Citation Analysis]
13 Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 2021;8:53. [PMID: 33816053 DOI: 10.1186/s40537-021-00444-8] [Cited by in Crossref: 29] [Cited by in F6Publishing: 12] [Article Influence: 29.0] [Reference Citation Analysis]
14 Alpsoy A, Yavuz A, Elpek GO. Artificial intelligence in pathological evaluation of gastrointestinal cancers. Artif Intell Gastroenterol 2021; 2(6): 141-156 [DOI: 10.35712/aig.v2.i6.141] [Reference Citation Analysis]
15 Li LS, Guo XY, Sun K. Recent advances in blood-based and artificial intelligence-enhanced approaches for gastrointestinal cancer diagnosis. World J Gastroenterol 2021; 27(34): 5666-5681 [PMID: 34629793 DOI: 10.3748/wjg.v27.i34.5666] [Reference Citation Analysis]
16 Topalovic N, Mazic S, Nesic D, Vukovic O, Cumic J, Laketic D, Stasevic Karlicic I, Pantic I. Association between Chromatin Structural Organization of Peripheral Blood Neutrophils and Self-Perceived Mental Stress: Gray-Level Co-occurrence Matrix Analysis. Microsc Microanal 2021;:1-7. [PMID: 34334154 DOI: 10.1017/S143192762101240X] [Reference Citation Analysis]
17 Mathew T, Ajith B, Kini JR, Rajan J. Deep learning‐based automated mitosis detection in histopathology images for breast cancer grading. Int J Imaging Syst Tech. [DOI: 10.1002/ima.22703] [Reference Citation Analysis]
18 Hong J, Li Q, Wang X, Li J, Ding W, Hu H, He L. Development and validation of apoptosis-related signature and molecular subtype to improve prognosis prediction in osteosarcoma patients. J Clin Lab Anal 2022;:e24501. [PMID: 35576501 DOI: 10.1002/jcla.24501] [Reference Citation Analysis]
19 Li C, Zong L, Zhong Y, Zhang M, Wang X. A study of colorectal cancer patients’ hair medulla by synchrotron radiation infrared microspectroscopy. Infrared Physics & Technology 2020;111:103569. [DOI: 10.1016/j.infrared.2020.103569] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Kobayashi S, Saltz JH, Yang VW. State of machine and deep learning in histopathological applications in digestive diseases. World J Gastroenterol 2021; 27(20): 2545-2575 [PMID: 34092975 DOI: 10.3748/wjg.v27.i20.2545] [Cited by in CrossRef: 1] [Article Influence: 1.0] [Reference Citation Analysis]