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For: Gertych A, Swiderska-Chadaj Z, Ma Z, Ing N, Markiewicz T, Cierniak S, Salemi H, Guzman S, Walts AE, Knudsen BS. Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides. Sci Rep 2019;9:1483. [PMID: 30728398 DOI: 10.1038/s41598-018-37638-9] [Cited by in Crossref: 49] [Cited by in F6Publishing: 40] [Article Influence: 16.3] [Reference Citation Analysis]
Number Citing Articles
1 Le Page AL, Ballot E, Truntzer C, Derangère V, Ilie A, Rageot D, Bibeau F, Ghiringhelli F. Using a convolutional neural network for classification of squamous and non-squamous non-small cell lung cancer based on diagnostic histopathology HES images. Sci Rep 2021;11:23912. [PMID: 34903781 DOI: 10.1038/s41598-021-03206-x] [Reference Citation Analysis]
2 Klimov S, Xue Y, Gertych A, Graham RP, Jiang Y, Bhattarai S, Pandol SJ, Rakha EA, Reid MD, Aneja R. Predicting Metastasis Risk in Pancreatic Neuroendocrine Tumors Using Deep Learning Image Analysis. Front Oncol 2020;10:593211. [PMID: 33718106 DOI: 10.3389/fonc.2020.593211] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
3 Sneider A, Kiemen A, Kim JH, Wu P, Habibi M, White M, Phillip JM, Gu L, Wirtz D. Deep learning identification of stiffness markers in breast cancer. Biomaterials 2022;285:121540. [DOI: 10.1016/j.biomaterials.2022.121540] [Reference Citation Analysis]
4 Wang S, Yang DM, Rong R, Zhan X, Fujimoto J, Liu H, Minna J, Wistuba II, Xie Y, Xiao G. Artificial Intelligence in Lung Cancer Pathology Image Analysis. Cancers (Basel) 2019;11:E1673. [PMID: 31661863 DOI: 10.3390/cancers11111673] [Cited by in Crossref: 33] [Cited by in F6Publishing: 30] [Article Influence: 11.0] [Reference Citation Analysis]
5 Prabhu S, Prasad K, Robels-kelly A, Lu X. AI-based carcinoma detection and classification using histopathological images: A systematic review. Computers in Biology and Medicine 2022;142:105209. [DOI: 10.1016/j.compbiomed.2022.105209] [Reference Citation Analysis]
6 Oliveira E Carmo L, van den Merkhof A, Olczak J, Gordon M, Jutte PC, Jaarsma RL, IJpma FFA, Doornberg JN, Prijs J; Machine Learning Consortium. An increasing number of convolutional neural networks for fracture recognition and classification in orthopaedics : are these externally validated and ready for clinical application? Bone Jt Open 2021;2:879-85. [PMID: 34669518 DOI: 10.1302/2633-1462.210.BJO-2021-0133] [Reference Citation Analysis]
7 Lee ALS, To CCK, Lee ALH, Li JJX, Chan RCK. Model architecture and tile size selection for convolutional neural network training for non-small cell lung cancer detection on whole slide images. Informatics in Medicine Unlocked 2022;28:100850. [DOI: 10.1016/j.imu.2022.100850] [Reference Citation Analysis]
8 Kanavati F, Toyokawa G, Momosaki S, Takeoka H, Okamoto M, Yamazaki K, Takeo S, Iizuka O, Tsuneki M. A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images. Sci Rep 2021;11:8110. [PMID: 33854137 DOI: 10.1038/s41598-021-87644-7] [Reference Citation Analysis]
9 Angayarkanni SP. Hybrid Convolution Neural Network in Classification of Cancer in Histopathology Images. J Digit Imaging 2022. [PMID: 35022925 DOI: 10.1007/s10278-021-00541-3] [Reference Citation Analysis]
10 Kriegsmann M, Haag C, Weis CA, Steinbuss G, Warth A, Zgorzelski C, Muley T, Winter H, Eichhorn ME, Eichhorn F, Kriegsmann J, Christopoulos P, Thomas M, Witzens-Harig M, Sinn P, von Winterfeld M, Heussel CP, Herth FJF, Klauschen F, Stenzinger A, Kriegsmann K. Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer. Cancers (Basel). 2020;12. [PMID: 32560475 DOI: 10.3390/cancers12061604] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
11 Bellini V, Valente M, Del Rio P, Bignami E. Artificial intelligence in thoracic surgery: a narrative review. J Thorac Dis 2021;13:6963-75. [PMID: 35070380 DOI: 10.21037/jtd-21-761] [Reference Citation Analysis]
12 Bera K, Katz I, Madabhushi A. Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology. JCO Clin Cancer Inform 2020;4:1039-50. [PMID: 33166198 DOI: 10.1200/CCI.20.00110] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
13 Kanavati F, Tsuneki M. A deep learning model for gastric diffuse-type adenocarcinoma classification in whole slide images. Sci Rep 2021;11:20486. [PMID: 34650155 DOI: 10.1038/s41598-021-99940-3] [Reference Citation Analysis]
14 Swiderska-chadaj Z, Ma Z, Ing N, Markiewicz T, Lorent M, Cierniak S, Walts AE, Knudsen BS, Gertych A. Contextual Classification of Tumor Growth Patterns in Digital Histology Slides. In: Pietka E, Badura P, Kawa J, Wieclawek W, editors. Information Technology in Biomedicine. Cham: Springer International Publishing; 2019. pp. 13-25. [DOI: 10.1007/978-3-030-23762-2_2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
15 Li Y, Chen D, Wu X, Yang W, Chen Y. A narrative review of artificial intelligence-assisted histopathologic diagnosis and decision-making for non-small cell lung cancer: achievements and limitations. J Thorac Dis 2021;13:7006-20. [PMID: 35070383 DOI: 10.21037/jtd-21-806] [Reference Citation Analysis]
16 Kanavati F, Ichihara S, Rambeau M, Iizuka O, Arihiro K, Tsuneki M. Deep Learning Models for Gastric Signet Ring Cell Carcinoma Classification in Whole Slide Images. Technol Cancer Res Treat 2021;20:15330338211027901. [PMID: 34191660 DOI: 10.1177/15330338211027901] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
17 Sakamoto T, Furukawa T, Lami K, Pham HHN, Uegami W, Kuroda K, Kawai M, Sakanashi H, Cooper LAD, Bychkov A, Fukuoka J. A narrative review of digital pathology and artificial intelligence: focusing on lung cancer. Transl Lung Cancer Res 2020;9:2255-76. [PMID: 33209648 DOI: 10.21037/tlcr-20-591] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
18 Ambrosini P, Hollemans E, Kweldam CF, Leenders GJLHV, Stallinga S, Vos F. Automated detection of cribriform growth patterns in prostate histology images. Sci Rep 2020;10:14904. [PMID: 32913202 DOI: 10.1038/s41598-020-71942-7] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
19 Mutasa S, Sun S, Ha R. Understanding artificial intelligence based radiology studies: CNN architecture. Clin Imaging 2021;80:72-6. [PMID: 34256218 DOI: 10.1016/j.clinimag.2021.06.033] [Reference Citation Analysis]
20 Tamiev D, Furman PE, Reuel NF. Automated classification of bacterial cell sub-populations with convolutional neural networks. PLoS One 2020;15:e0241200. [PMID: 33104721 DOI: 10.1371/journal.pone.0241200] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
21 Steinbuss G, Kriegsmann K, Kriegsmann M. Identification of Gastritis Subtypes by Convolutional Neuronal Networks on Histological Images of Antrum and Corpus Biopsies. Int J Mol Sci. 2020;21. [PMID: 32932860 DOI: 10.3390/ijms21186652] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
22 Muñoz-Aguirre M, Ntasis VF, Rojas S, Guigó R. PyHIST: A Histological Image Segmentation Tool. PLoS Comput Biol 2020;16:e1008349. [PMID: 33075075 DOI: 10.1371/journal.pcbi.1008349] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
23 Azuaje F, Kim SY, Perez Hernandez D, Dittmar G. Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning. J Clin Med 2019;8:E1535. [PMID: 31557788 DOI: 10.3390/jcm8101535] [Cited by in Crossref: 11] [Cited by in F6Publishing: 7] [Article Influence: 3.7] [Reference Citation Analysis]
24 Mohlman JS, Leventhal SD, Hansen T, Kohan J, Pascucci V, Salama ME. Improving Augmented Human Intelligence to Distinguish Burkitt Lymphoma From Diffuse Large B-Cell Lymphoma Cases. Am J Clin Pathol 2020;153:743-59. [PMID: 32067039 DOI: 10.1093/ajcp/aqaa001] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
25 Gallego J, Swiderska-Chadaj Z, Markiewicz T, Yamashita M, Gabaldon MA, Gertych A. A U-Net based framework to quantify glomerulosclerosis in digitized PAS and H&E stained human tissues. Comput Med Imaging Graph 2021;89:101865. [PMID: 33548823 DOI: 10.1016/j.compmedimag.2021.101865] [Reference Citation Analysis]
26 Kanavati F, Tsuneki M. Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning. Cancers (Basel) 2021;13:5368. [PMID: 34771530 DOI: 10.3390/cancers13215368] [Reference Citation Analysis]
27 Nishio M, Nishio M, Jimbo N, Nakane K. Homology-Based Image Processing for Automatic Classification of Histopathological Images of Lung Tissue. Cancers (Basel) 2021;13:1192. [PMID: 33801859 DOI: 10.3390/cancers13061192] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
28 Yang H, Chen L, Cheng Z, Yang M, Wang J, Lin C, Wang Y, Huang L, Chen Y, Peng S, Ke Z, Li W. Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study. BMC Med 2021;19:80. [PMID: 33775248 DOI: 10.1186/s12916-021-01953-2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
29 Tsuneki M, Kanavati F. Deep Learning Models for Poorly Differentiated Colorectal Adenocarcinoma Classification in Whole Slide Images Using Transfer Learning. Diagnostics (Basel) 2021;11:2074. [PMID: 34829419 DOI: 10.3390/diagnostics11112074] [Reference Citation Analysis]
30 Jiang Y, Yang M, Wang S, Li X, Sun Y. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun (Lond). 2020;40:154-166. [PMID: 32277744 DOI: 10.1002/cac2.12012] [Cited by in Crossref: 17] [Cited by in F6Publishing: 18] [Article Influence: 8.5] [Reference Citation Analysis]
31 McCombe KD, Craig SG, Viratham Pulsawatdi A, Quezada-Marín JI, Hagan M, Rajendran S, Humphries MP, Bingham V, Salto-Tellez M, Gault R, James JA. HistoClean: Open-source software for histological image pre-processing and augmentation to improve development of robust convolutional neural networks. Comput Struct Biotechnol J 2021;19:4840-53. [PMID: 34522291 DOI: 10.1016/j.csbj.2021.08.033] [Reference Citation Analysis]
32 Nakajima N, Yoshizawa A, Rokutan-Kurata M, Noguchi M, Teramoto Y, Sumiyoshi S, Kondo K, Sonobe M, Hamaji M, Menju T, Date H, Haga H. Prognostic significance of cribriform adenocarcinoma of the lung: validation analysis of 1,057 Japanese patients with resected lung adenocarcinoma and a review of the literature. Transl Lung Cancer Res 2021;10:117-27. [PMID: 33569298 DOI: 10.21037/tlcr-20-612] [Reference Citation Analysis]
33 Zhang X, Lu S, Wang S, Yu X, Wang S, Yao L, Pan Y, Zhang Y. Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture. J Comput Sci Technol 2022;37:330-43. [DOI: 10.1007/s11390-020-0679-8] [Reference Citation Analysis]
34 Dominguez GA, Polo AT, Roop J, Campisi AJ, Somer RA, Perzin AD, Gabrilovich DI, Kumar A. Detecting Prostate Cancer Using Pattern Recognition Neural Networks With Flow Cytometry-Based Immunophenotyping in At-Risk Men. Biomark Insights 2020;15:1177271920913320. [PMID: 32341637 DOI: 10.1177/1177271920913320] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
35 She Y, Jin Z, Wu J, Deng J, Zhang L, Su H, Jiang G, Liu H, Xie D, Cao N, Ren Y, Chen C. Development and Validation of a Deep Learning Model for Non-Small Cell Lung Cancer Survival. JAMA Netw Open 2020;3:e205842. [PMID: 32492161 DOI: 10.1001/jamanetworkopen.2020.5842] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
36 Crouzet C, Jeong G, Chae RH, LoPresti KT, Dunn CE, Xie DF, Agu C, Fang C, Nunes ACF, Lau WL, Kim S, Cribbs DH, Fisher M, Choi B. Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages. Sci Rep 2021;11:10725. [PMID: 34021170 DOI: 10.1038/s41598-021-88236-1] [Reference Citation Analysis]
37 Bándi P, Balkenhol M, van Ginneken B, van der Laak J, Litjens G. Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks. PeerJ 2019;7:e8242. [PMID: 31871843 DOI: 10.7717/peerj.8242] [Cited by in Crossref: 9] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
38 Kanavati F, Toyokawa G, Momosaki S, Rambeau M, Kozuma Y, Shoji F, Yamazaki K, Takeo S, Iizuka O, Tsuneki M. Weakly-supervised learning for lung carcinoma classification using deep learning. Sci Rep. 2020;10:9297. [PMID: 32518413 DOI: 10.1038/s41598-020-66333-x] [Cited by in Crossref: 19] [Cited by in F6Publishing: 15] [Article Influence: 9.5] [Reference Citation Analysis]
39 Lancellotti C, Cancian P, Savevski V, Kotha SRR, Fraggetta F, Graziano P, Di Tommaso L. Artificial Intelligence & Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology. Cells 2021;10:787. [PMID: 33918173 DOI: 10.3390/cells10040787] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
40 Su Y, Xiang H, Xie H, Yu Y, Dong S, Yang Z, Zhao N. Application of BERT to Enable Gene Classification Based on Clinical Evidence. Biomed Res Int 2020;2020:5491963. [PMID: 33083472 DOI: 10.1155/2020/5491963] [Reference Citation Analysis]
41 Kanavati F, Ichihara S, Tsuneki M. A deep learning model for breast ductal carcinoma in situ classification in whole slide images. Virchows Arch 2022. [PMID: 35076741 DOI: 10.1007/s00428-021-03241-z] [Reference Citation Analysis]
42 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]
43 Martinez SJ, Romano PS, Engman DM. Precision Health for Chagas Disease: Integrating Parasite and Host Factors to Predict Outcome of Infection and Response to Therapy. Front Cell Infect Microbiol 2020;10:210. [PMID: 32457849 DOI: 10.3389/fcimb.2020.00210] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
44 Tolkach Y, Dohmgörgen T, Toma M, Kristiansen G. High-accuracy prostate cancer pathology using deep learning. Nat Mach Intell 2020;2:411-8. [DOI: 10.1038/s42256-020-0200-7] [Cited by in Crossref: 8] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
45 Klimov S, Miligy IM, Gertych A, Jiang Y, Toss MS, Rida P, Ellis IO, Green A, Krishnamurti U, Rakha EA, Aneja R. A whole slide image-based machine learning approach to predict ductal carcinoma in situ (DCIS) recurrence risk. Breast Cancer Res 2019;21:83. [PMID: 31358020 DOI: 10.1186/s13058-019-1165-5] [Cited by in Crossref: 14] [Cited by in F6Publishing: 14] [Article Influence: 4.7] [Reference Citation Analysis]
46 Halicek M, Shahedi M, Little JV, Chen AY, Myers LL, Sumer BD, Fei B. Head and Neck Cancer Detection in Digitized Whole-Slide Histology Using Convolutional Neural Networks. Sci Rep 2019;9:14043. [PMID: 31575946 DOI: 10.1038/s41598-019-50313-x] [Cited by in Crossref: 15] [Cited by in F6Publishing: 10] [Article Influence: 5.0] [Reference Citation Analysis]
47 Naito Y, Tsuneki M, Fukushima N, Koga Y, Higashi M, Notohara K, Aishima S, Ohike N, Tajiri T, Yamaguchi H, Fukumura Y, Kojima M, Hirabayashi K, Hamada Y, Norose T, Kai K, Omori Y, Sukeda A, Noguchi H, Uchino K, Itakura J, Okabe Y, Yamada Y, Akiba J, Kanavati F, Oda Y, Furukawa T, Yano H. A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy. Sci Rep 2021;11:8454. [PMID: 33875703 DOI: 10.1038/s41598-021-87748-0] [Reference Citation Analysis]
48 Shim WS, Yim K, Kim TJ, Sung YE, Lee G, Hong JH, Chun SH, Kim S, An HJ, Na SJ, Kim JJ, Moon MH, Moon SW, Park S, Hong SA, Ko YH. DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks. Cancers (Basel) 2021;13:3308. [PMID: 34282757 DOI: 10.3390/cancers13133308] [Reference Citation Analysis]
49 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]
50 Song J, Ding C, Huang Q, Luo T, Xu X, Chen Z, Li S. Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma. Med Phys 2021. [PMID: 34669994 DOI: 10.1002/mp.15307] [Reference Citation Analysis]
51 Sha L, Osinski BL, Ho IY, Tan TL, Willis C, Weiss H, Beaubier N, Mahon BM, Taxter TJ, Yip SSF. Multi-Field-of-View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death-Ligand 1 Status from Whole-Slide Hematoxylin and Eosin Images. J Pathol Inform 2019;10:24. [PMID: 31523482 DOI: 10.4103/jpi.jpi_24_19] [Cited by in Crossref: 24] [Cited by in F6Publishing: 23] [Article Influence: 8.0] [Reference Citation Analysis]
52 Pham TD. Time-frequency time-space long short-term memory networks for image classification of histopathological tissue. Sci Rep 2021;11:13703. [PMID: 34211077 DOI: 10.1038/s41598-021-93160-5] [Reference Citation Analysis]
53 Venerito V, Angelini O, Cazzato G, Lopalco G, Maiorano E, Cimmino A, Iannone F. A convolutional neural network with transfer learning for automatic discrimination between low and high-grade synovitis: a pilot study. Intern Emerg Med 2021;16:1457-65. [PMID: 33387201 DOI: 10.1007/s11739-020-02583-x] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
54 Tsuneki M. Deep learning models in medical image analysis. J Oral Biosci 2022:S1349-0079(22)00050-0. [PMID: 35306172 DOI: 10.1016/j.job.2022.03.003] [Reference Citation Analysis]
55 Gonem S, Janssens W, Das N, Topalovic M. Applications of artificial intelligence and machine learning in respiratory medicine. Thorax. 2020;75:695-701. [PMID: 32409611 DOI: 10.1136/thoraxjnl-2020-214556] [Cited by in Crossref: 12] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
56 Shavlokhova V, Sandhu S, Flechtenmacher C, Koveshazi I, Neumeier F, Padrón-Laso V, Jonke Ž, Saravi B, Vollmer M, Vollmer A, Hoffmann J, Engel M, Ristow O, Freudlsperger C. Deep Learning on Oral Squamous Cell Carcinoma Ex Vivo Fluorescent Confocal Microscopy Data: A Feasibility Study. J Clin Med 2021;10:5326. [PMID: 34830608 DOI: 10.3390/jcm10225326] [Reference Citation Analysis]
57 Tsuneki M, Abe M, Kanavati F. A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning. Diagnostics (Basel) 2022;12:768. [PMID: 35328321 DOI: 10.3390/diagnostics12030768] [Reference Citation Analysis]