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For: van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat Med 2021;27:775-84. [PMID: 33990804 DOI: 10.1038/s41591-021-01343-4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Hassan T, Javed S, Mahmood A, Qaiser T, Werghi N, Rajpoot N. Nucleus Classification in Histology Images Using Message Passing Network. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102480] [Reference Citation Analysis]
2 Dai C, Wang S, Mo Y, Angelini E, Guo Y, Bai W. Suggestive Annotation of Brain MR Images with Gradient-guided Sampling. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102373] [Reference Citation Analysis]
3 Flach RN, Fransen NL, Sonnen AFP, Nguyen TQ, Breimer GE, Veta M, Stathonikos N, van Dooijeweert C, van Diest PJ. Implementation of Artificial Intelligence in Diagnostic Practice as a Next Step after Going Digital: The UMC Utrecht Perspective. Diagnostics 2022;12:1042. [DOI: 10.3390/diagnostics12051042] [Reference Citation Analysis]
4 Lee K, Lockhart JH, Xie M, Chaudhary R, Slebos RJC, Flores ER, Chung CH, Tan AC. Deep Learning of Histopathology Images at the Single Cell Level. Front Artif Intell 2021;4:754641. [PMID: 34568816 DOI: 10.3389/frai.2021.754641] [Reference Citation Analysis]
5 Ahmedt-aristizabal D, Armin MA, Denman S, Fookes C, Petersson L. A survey on graph-based deep learning for computational histopathology. Computerized Medical Imaging and Graphics 2022;95:102027. [DOI: 10.1016/j.compmedimag.2021.102027] [Reference Citation Analysis]
6 Javed S, Mahmood A, Dias J, Werghi N. Multi-level feature fusion for nucleus detection in histology images using correlation filters. Computers in Biology and Medicine 2022;143:105281. [DOI: 10.1016/j.compbiomed.2022.105281] [Reference Citation Analysis]
7 Konstantinov AV, Utkin LV. Multi-attention multiple instance learning. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07259-5] [Reference Citation Analysis]
8 Baxi V, Edwards R, Montalto M, Saha S. Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol 2021. [PMID: 34611303 DOI: 10.1038/s41379-021-00919-2] [Reference Citation Analysis]
9 Jiménez-sánchez D, Ariz M, Chang H, Matias-guiu X, de Andrea CE, Ortiz-de-solórzano C. NaroNet: discovery of tumor microenvironment elements from highly multiplexed images. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102384] [Reference Citation Analysis]
10 Cheng S, Liu S, Yu J, Rao G, Xiao Y, Han W, Zhu W, Lv X, Li N, Cai J, Wang Z, Feng X, Yang F, Geng X, Ma J, Li X, Wei Z, Zhang X, Quan T, Zeng S, Chen L, Hu J, Liu X. Robust whole slide image analysis for cervical cancer screening using deep learning. Nat Commun 2021;12:5639. [PMID: 34561435 DOI: 10.1038/s41467-021-25296-x] [Reference Citation Analysis]
11 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]
12 Ali M, Ali R. Multi-Input Dual-Stream Capsule Network for Improved Lung and Colon Cancer Classification. Diagnostics (Basel) 2021;11:1485. [PMID: 34441419 DOI: 10.3390/diagnostics11081485] [Reference Citation Analysis]
13 Su A, Lee H, Tan X, Suarez CJ, Andor N, Nguyen Q, Ji HP. A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images. NPJ Precis Oncol 2022;6:14. [PMID: 35236916 DOI: 10.1038/s41698-022-00252-0] [Reference Citation Analysis]
14 Yamaki T, Sukhbaatar A, Mishra R, Kikuchi R, Sakamoto M, Mori S, Kodama T. Characterizing perfusion defects in metastatic lymph nodes at an early stage using high-frequency ultrasound and micro-CT imaging. Clin Exp Metastasis 2021;38:539-49. [PMID: 34654990 DOI: 10.1007/s10585-021-10127-6] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Ringborg U, Berns A, Celis JE, Heitor M, Tabernero J, Schüz J, Baumann M, Henrique R, Aapro M, Basu P, Beets-Tan R, Besse B, Cardoso F, Carneiro F, van den Eede G, Eggermont A, Fröhling S, Galbraith S, Garralda E, Hanahan D, Hofmarcher T, Jönsson B, Kallioniemi O, Kásler M, Kondorosi E, Korbel J, Lacombe D, Carlos Machado J, Martin-Moreno JM, Meunier F, Nagy P, Nuciforo P, Oberst S, Oliveiera J, Papatriantafyllou M, Ricciardi W, Roediger A, Ryll B, Schilsky R, Scocca G, Seruca R, Soares M, Steindorf K, Valentini V, Voest E, Weiderpass E, Wilking N, Wren A, Zitvogel L. The Porto European Cancer Research Summit 2021. Mol Oncol 2021;15:2507-43. [PMID: 34515408 DOI: 10.1002/1878-0261.13078] [Reference Citation Analysis]
16 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]
17 Wharton KA Jr, Wood D, Manesse M, Maclean KH, Leiss F, Zuraw A. Tissue Multiplex Analyte Detection in Anatomic Pathology - Pathways to Clinical Implementation. Front Mol Biosci 2021;8:672531. [PMID: 34386519 DOI: 10.3389/fmolb.2021.672531] [Reference Citation Analysis]
18 Vesterinen T, Säilä J, Blom S, Pennanen M, Leijon H, Arola J. Automated assessment of Ki-67 proliferation index in neuroendocrine tumors by deep learning. APMIS 2021. [PMID: 34741788 DOI: 10.1111/apm.13190] [Reference Citation Analysis]
19 Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med 2022. [PMID: 35058619 DOI: 10.1038/s41591-021-01614-0] [Reference Citation Analysis]
20 de Bel T, Litjens G, Ogony J, Stallings-Mann M, Carter JM, Hilton T, Radisky DC, Vierkant RA, Broderick B, Hoskin TL, Winham SJ, Frost MH, Visscher DW, Allers T, Degnim AC, Sherman ME, van der Laak JAWM. Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning. NPJ Breast Cancer 2022;8:13. [PMID: 35046392 DOI: 10.1038/s41523-021-00378-7] [Reference Citation Analysis]
21 Zhang C, Gu J, Zhu Y, Meng Z, Tong T, Li D, Liu Z, Du Y, Wang K, Tian J. AI in spotting high-risk characteristics of medical imaging and molecular pathology. Precision Clinical Medicine 2021;4:271-86. [DOI: 10.1093/pcmedi/pbab026] [Reference Citation Analysis]
22 Glass C, Lafata KJ, Jeck W, Horstmeyer R, Cooke C, Everitt J, Glass M, Dov D, Seidman MA. The Role of Machine Learning in Cardiovascular Pathology. Can J Cardiol 2021:S0828-282X(21)00867-9. [PMID: 34813876 DOI: 10.1016/j.cjca.2021.11.008] [Reference Citation Analysis]
23 Haddad TS, Friedl P, Farahani N, Treanor D, Zlobec I, Nagtegaal I. Tutorial: methods for three-dimensional visualization of archival tissue material. Nat Protoc 2021;16:4945-62. [PMID: 34716449 DOI: 10.1038/s41596-021-00611-4] [Reference Citation Analysis]
24 Pettersen HS, Belevich I, Røyset ES, Smistad E, Simpson MR, Jokitalo E, Reinertsen I, Bakke I, Pedersen A. Code-Free Development and Deployment of Deep Segmentation Models for Digital Pathology. Front Med 2022;8:816281. [DOI: 10.3389/fmed.2021.816281] [Reference Citation Analysis]
25 Sandeman K, Blom S, Koponen V, Manninen A, Juhila J, Rannikko A, Ropponen T, Mirtti T. AI Model for Prostate Biopsies Predicts Cancer Survival. Diagnostics 2022;12:1031. [DOI: 10.3390/diagnostics12051031] [Reference Citation Analysis]
26 Bouaoud J, Bossi P, Elkabets M, Schmitz S, van Kempen LC, Martinez P, Jagadeeshan S, Breuskin I, Puppels GJ, Hoffmann C, Hunter KD, Simon C, Machiels J, Grégoire V, Bertolus C, Brakenhoff RH, Koljenović S, Saintigny P. Unmet Needs and Perspectives in Oral Cancer Prevention. Cancers 2022;14:1815. [DOI: 10.3390/cancers14071815] [Reference Citation Analysis]
27 Sherman ME, de Bel T, Heckman MG, White LJ, Ogony J, Stallings-Mann M, Hilton T, Degnim AC, Vierkant RA, Hoskin T, Jensen MR, Pacheco-Spann L, Henry JE, Storniolo AM, Carter JM, Winham SJ, Radisky DC, van der Laak J. Serum hormone levels and normal breast histology among premenopausal women. Breast Cancer Res Treat 2022. [PMID: 35503494 DOI: 10.1007/s10549-022-06600-9] [Reference Citation Analysis]
28 Zheng Z, Zhang X, Ding J, Zhang D, Cui J, Fu X, Han J, Zhu P. Deep Learning-Based Artificial Intelligence System for Automatic Assessment of Glomerular Pathological Findings in Lupus Nephritis. Diagnostics (Basel) 2021;11:1983. [PMID: 34829330 DOI: 10.3390/diagnostics11111983] [Reference Citation Analysis]
29 Marini N, Otálora S, Podareanu D, van Rijthoven M, van der Laak J, Ciompi F, Müller H, Atzori M. Multi_Scale_Tools: A Python Library to Exploit Multi-Scale Whole Slide Images. Front Comput Sci 2021;3:684521. [DOI: 10.3389/fcomp.2021.684521] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
30 Kim RH, Nomikou S, Coudray N, Jour G, Dawood Z, Hong R, Esteva E, Sakellaropoulos T, Donnelly D, Moran U, Hatzimemos A, Weber JS, Razavian N, Aifantis I, Fenyo D, Snuderl M, Shapiro R, Berman RS, Osman I, Tsirigos A. Deep learning and pathomics analyses reveal cell nuclei as important features for mutation prediction of BRAF-mutated melanomas. J Invest Dermatol 2021:S0022-202X(21)02405-2. [PMID: 34757067 DOI: 10.1016/j.jid.2021.09.034] [Reference Citation Analysis]