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For: Komura D, Ishikawa S. Machine learning approaches for pathologic diagnosis. Virchows Arch. 2019;475:131-138. [PMID: 31222375 DOI: 10.1007/s00428-019-02594-w] [Cited by in Crossref: 25] [Cited by in F6Publishing: 22] [Article Influence: 8.3] [Reference Citation Analysis]
Number Citing Articles
1 Qin T, Zhu Z, Wang XS, Xia J, Wu S. Computational representations of protein-ligand interfaces for structure-based virtual screening. Expert Opin Drug Discov 2021;:1-18. [PMID: 34011222 DOI: 10.1080/17460441.2021.1929921] [Reference Citation Analysis]
2 Lino-Silva LS, Xinaxtle DL. Artificial intelligence technology applications in the pathologic diagnosis of the gastrointestinal tract. Future Oncol 2020;16:2845-51. [PMID: 32892631 DOI: 10.2217/fon-2020-0678] [Reference Citation Analysis]
3 Wang X, Chen P, Ding G, Xing Y, Tang R, Peng C, Ye Y, Fu Q. Dual-scale categorization based deep learning to evaluate programmed cell death ligand 1 expression in non-small cell lung cancer. Medicine (Baltimore) 2021;100:e25994. [PMID: 34011092 DOI: 10.1097/MD.0000000000025994] [Reference Citation Analysis]
4 Cui M, Zhang DY. Artificial intelligence and computational pathology. Lab Invest 2021;101:412-22. [PMID: 33454724 DOI: 10.1038/s41374-020-00514-0] [Cited by in Crossref: 12] [Cited by in F6Publishing: 7] [Article Influence: 12.0] [Reference Citation Analysis]
5 Paredes BE. [Pattern analysis of inflammatory skin diseases according to A. B. Ackerman-always up to date]. Pathologe 2020;41:301-16. [PMID: 32377832 DOI: 10.1007/s00292-020-00789-6] [Reference Citation Analysis]
6 Kim WJ, Jin P, Kim WH, Kim J. Utilizing machine learning to discern hidden clinical values from big data in urology. Investig Clin Urol 2020;61:239-41. [PMID: 32377598 DOI: 10.4111/icu.2020.61.3.239] [Reference Citation Analysis]
7 Blaivas L, Blaivas M. Are Convolutional Neural Networks Trained on ImageNet Images Wearing Rose-Colored Glasses?: A Quantitative Comparison of ImageNet, Computed Tomographic, Magnetic Resonance, Chest X-Ray, and Point-of-Care Ultrasound Images for Quality. J Ultrasound Med 2021;40:377-83. [PMID: 32757235 DOI: 10.1002/jum.15413] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
8 Rahman A, Jahangir C, Lynch SM, Alattar N, Aura C, Russell N, Lanigan F, Gallagher WM. Advances in tissue-based imaging: impact on oncology research and clinical practice. Expert Rev Mol Diagn 2020;20:1027-37. [PMID: 32510287 DOI: 10.1080/14737159.2020.1770599] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Blaivas M, Blaivas LN, Campbell K, Thomas J, Shah S, Yadav K, Liu YT. Making Artificial Intelligence Lemonade Out of Data Lemons: Adaptation of a Public Apical Echo Database for Creation of a Subxiphoid Visual Estimation Automatic Ejection Fraction Machine Learning Algorithm. J Ultrasound Med 2021. [PMID: 34820867 DOI: 10.1002/jum.15889] [Reference Citation Analysis]
10 Liu PR, Lu L, Zhang JY, Huo TT, Liu SX, Ye ZW. Application of Artificial Intelligence in Medicine: An Overview. Curr Med Sci 2021. [PMID: 34874486 DOI: 10.1007/s11596-021-2474-3] [Reference Citation Analysis]
11 Huss R, Coupland SE. Software‐assisted decision support in digital histopathology. J Pathol 2020;250:685-92. [DOI: 10.1002/path.5388] [Cited by in Crossref: 15] [Cited by in F6Publishing: 14] [Article Influence: 7.5] [Reference Citation Analysis]
12 Morales S, Engan K, Naranjo V. Artificial intelligence in computational pathology – challenges and future directions. Digital Signal Processing 2021;119:103196. [DOI: 10.1016/j.dsp.2021.103196] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Krithiga R, Geetha P. Breast Cancer Detection, Segmentation and Classification on Histopathology Images Analysis: A Systematic Review. Arch Computat Methods Eng 2021;28:2607-19. [DOI: 10.1007/s11831-020-09470-w] [Cited by in Crossref: 9] [Article Influence: 4.5] [Reference Citation Analysis]
14 Wu Y, Cheng M, Huang S, Pei Z, Zuo Y, Liu J, Yang K, Zhu Q, Zhang J, Hong H, Zhang D, Huang K, Cheng L, Shao W. Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications. Cancers 2022;14:1199. [DOI: 10.3390/cancers14051199] [Reference Citation Analysis]
15 Klamminger GG, Gérardy JJ, Jelke F, Mirizzi G, Slimani R, Klein K, Husch A, Hertel F, Mittelbronn M, Kleine-Borgmann FB. Application of Raman spectroscopy for detection of histologically distinct areas in formalin-fixed paraffin-embedded glioblastoma. Neurooncol Adv 2021;3:vdab077. [PMID: 34355170 DOI: 10.1093/noajnl/vdab077] [Reference Citation Analysis]
16 Liu W, Wang S, Ye Z, Xu P, Xia X, Guo M. Prediction of lung metastases in thyroid cancer using machine learning based on SEER database. Cancer Med 2022. [PMID: 35191613 DOI: 10.1002/cam4.4617] [Reference Citation Analysis]
17 Asai A, Konno M, Taniguchi M, Vecchione A, Ishii H. Computational healthcare: Present and future perspectives (Review). Exp Ther Med 2021;22:1351. [PMID: 34659497 DOI: 10.3892/etm.2021.10786] [Reference Citation Analysis]
18 Park CW, Seo SW, Kang N, Ko B, Choi BW, Park CM, Chang DK, Kim H, Kim H, Lee H, Jang J, Ye JC, Jeon JH, Seo JB, Kim KJ, Jung KH, Kim N, Paek S, Shin SY, Yoo S, Choi YS, Kim Y, Yoon HJ. Artificial Intelligence in Health Care: Current Applications and Issues. J Korean Med Sci 2020;35:e379. [PMID: 33140591 DOI: 10.3346/jkms.2020.35.e379] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
19 Solanki SL, Pandrowala S, Nayak A, Bhandare M, Ambulkar RP, Shrikhande SV. Artificial intelligence in perioperative management of major gastrointestinal surgeries. World J Gastroenterol 2021; 27(21): 2758-2770 [PMID: 34135552 DOI: 10.3748/wjg.v27.i21.2758] [Reference Citation Analysis]
20 De Logu F, Ugolini F, Maio V, Simi S, Cossu A, Massi D, Nassini R, Laurino M; Italian Association for Cancer Research (AIRC) Study Group. Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm. Front Oncol 2020;10:1559. [PMID: 33014803 DOI: 10.3389/fonc.2020.01559] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
21 Schwen LO, Schacherer D, Geißler C, Homeyer A. Evaluating generic AutoML tools for computational pathology. Informatics in Medicine Unlocked 2022. [DOI: 10.1016/j.imu.2022.100853] [Reference Citation Analysis]
22 Pallua JD, Brunner A, Zelger B, Schirmer M, Haybaeck J. The future of pathology is digital. Pathol Res Pract 2020;216:153040. [PMID: 32825928 DOI: 10.1016/j.prp.2020.153040] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
23 Jiang L, Chen W, Dong B, Mei K, Zhu C, Liu J, Cai M, Yan Y, Wang G, Zuo L, Shi H. A Deep Learning-Based Approach for Glomeruli Instance Segmentation from Multistained Renal Biopsy Pathologic Images. Am J Pathol 2021;191:1431-41. [PMID: 34294192 DOI: 10.1016/j.ajpath.2021.05.004] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
24 Juarez-Flores A, Zamudio GS, José MV. Novel gene signatures for stage classification of the squamous cell carcinoma of the lung. Sci Rep 2021;11:4835. [PMID: 33649335 DOI: 10.1038/s41598-021-83668-1] [Reference Citation Analysis]
25 Fu T, Dai LJ, Wu SY, Xiao Y, Ma D, Jiang YZ, Shao ZM. Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response. J Hematol Oncol 2021;14:98. [PMID: 34172088 DOI: 10.1186/s13045-021-01103-4] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
26 Slumstrup L, Eiholm S, Bennedsen ALB, Jepsen DNM, Gögenur I, Fiehn AK. Deeper sections reveal residual tumor cells in rectal cancer specimens diagnosed with pathological complete response following neoadjuvant treatment. Virchows Arch. [DOI: 10.1007/s00428-022-03287-7] [Reference Citation Analysis]
27 Nazha A. Can the computer see what the human sees? Blood 2021;138:1907-8. [PMID: 34792569 DOI: 10.1182/blood.2021013259] [Reference Citation Analysis]
28 Cascini F, De Giovanni N, Inserra I, Santaroni F, Laura L. A data-driven methodology to discover similarities between cocaine samples. Sci Rep 2020;10:15976. [PMID: 32994485 DOI: 10.1038/s41598-020-72652-w] [Reference Citation Analysis]
29 López-Janeiro Á, Cabañuz C, Blasco-Santana L, Ruiz-Bravo E. A tree-based machine learning model to approach morphologic assessment of malignant salivary gland tumors. Ann Diagn Pathol 2021;56:151869. [PMID: 34823074 DOI: 10.1016/j.anndiagpath.2021.151869] [Reference Citation Analysis]
30 Cheng JY, Abel JT, Balis UGJ, McClintock DS, Pantanowitz L. Challenges in the Development, Deployment, and Regulation of Artificial Intelligence in Anatomic Pathology. Am J Pathol. 2020;. [PMID: 33245914 DOI: 10.1016/j.ajpath.2020.10.018] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
31 Xia X, Feng B, Wang J, Hua Q, Yang Y, Sheng L, Mou Y, Hu W. Deep Learning for Differentiating Benign From Malignant Parotid Lesions on MR Images. Front Oncol 2021;11:632104. [PMID: 34249680 DOI: 10.3389/fonc.2021.632104] [Reference Citation Analysis]
32 Radakovich N, Nagy M, Nazha A. Machine learning in haematological malignancies. Lancet Haematol 2020;7:e541-50. [PMID: 32589980 DOI: 10.1016/S2352-3026(20)30121-6] [Cited by in Crossref: 12] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
33 Fawcett TJ, Cooper CS, Longenecker RJ, Walton JP. Automated classification of acoustic startle reflex waveforms in young CBA/CaJ mice using machine learning. J Neurosci Methods 2020;344:108853. [PMID: 32668315 DOI: 10.1016/j.jneumeth.2020.108853] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]