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For: 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]
Number Citing Articles
1 Yang P, Yin X, Lu H, Hu Z, Zhang X, Jiang R, Lv H. CS-CO: A Hybrid Self-Supervised Visual Representation Learning Method for H&E-stained Histopathological Images. Medical Image Analysis 2022;81:102539. [DOI: 10.1016/j.media.2022.102539] [Reference Citation Analysis]
2 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]
3 Isberg OG, Giunchiglia V, Mckenzie JS, Takats Z, Jonasson JG, Bodvarsdottir SK, Thorsteinsdottir M, Xiang Y. Automated Cancer Diagnostics via Analysis of Optical and Chemical Images by Deep and Shallow Learning. Metabolites 2022;12:455. [DOI: 10.3390/metabo12050455] [Reference Citation Analysis]
4 Fisher NC, Loughrey MB, Coleman HG, Gelbard MD, Bankhead P, Dunne PD. Development of a semi-automated method for tumour budding assessment in colorectal cancer and comparison with manual methods. Histopathology 2021. [PMID: 34580909 DOI: 10.1111/his.14574] [Reference Citation Analysis]
5 Chantziantoniou N. BestCyte® Cell Sorter Imaging System: Primary and Adjudicative Whole Slide Image Rescreening Review Times of 500 ThinPrep Pap Test Thin-layers - An Intra-observer, Time-Surrogate Analysis of Diagnostic Confidence Potentialities. Journal of Pathology Informatics 2022. [DOI: 10.1016/j.jpi.2022.100095] [Reference Citation Analysis]
6 Appakkannu A, Govindaraj E, Balakrishnan K. Detection of Abnormality in Prostate Tissues Using Two-dimensional Photonic Crystal Tactile Sensor. Plasmonics. [DOI: 10.1007/s11468-022-01635-6] [Reference Citation Analysis]
7 Komori T. Grading of adult diffuse gliomas according to the 2021 WHO Classification of Tumors of the Central Nervous System. Lab Invest 2021. [PMID: 34504304 DOI: 10.1038/s41374-021-00667-6] [Reference Citation Analysis]
8 Chen S, Lakshmanna K. Application and Thinking of Artificial Intelligence in Electrical Automation. Wireless Communications and Mobile Computing 2022;2022:1-6. [DOI: 10.1155/2022/2609156] [Reference Citation Analysis]
9 Fiehn AK, Reiss B, Gögenur M, Bzorek M, Gögenur I. Development of a Fully Automated Method to Obtain Reproducible Lymphocyte Counts in Patients With Colorectal Cancer. Appl Immunohistochem Mol Morphol 2022. [PMID: 35703148 DOI: 10.1097/PAI.0000000000001041] [Reference Citation Analysis]
10 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]
11 Chen X, Zhao J, Iselin KC, Borroni D, Romano D, Gokul A, McGhee CNJ, Zhao Y, Sedaghat MR, Momeni-Moghaddam H, Ziaei M, Kaye S, Romano V, Zheng Y. Keratoconus detection of changes using deep learning of colour-coded maps. BMJ Open Ophthalmol 2021;6:e000824. [PMID: 34337155 DOI: 10.1136/bmjophth-2021-000824] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
12 Larsen BT. Usual interstitial pneumonia: a clinically significant pattern, but not the final word. Mod Pathol 2022. [PMID: 35210554 DOI: 10.1038/s41379-022-01054-2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Ho C, Zhao Z, Chen XF, Sauer J, Saraf SA, Jialdasani R, Taghipour K, Sathe A, Khor LY, Lim KH, Leow WQ. A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer. Sci Rep 2022;12:2222. [PMID: 35140318 DOI: 10.1038/s41598-022-06264-x] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
14 Yeung MCF, Cheng ISY. Artificial intelligence significantly improves the diagnostic accuracy of deep myxoid soft tissue lesions in histology. Sci Rep 2022;12:6965. [PMID: 35484289 DOI: 10.1038/s41598-022-11009-x] [Reference Citation Analysis]
15 Kwong GA, Ghosh S, Gamboa L, Patriotis C, Srivastava S, Bhatia SN. Synthetic biomarkers: a twenty-first century path to early cancer detection. Nat Rev Cancer 2021;21:655-68. [PMID: 34489588 DOI: 10.1038/s41568-021-00389-3] [Reference Citation Analysis]
16 Kemp JA, Kwon YJ. Cancer nanotechnology: current status and perspectives. Nano Converg 2021;8:34. [PMID: 34727233 DOI: 10.1186/s40580-021-00282-7] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Rabbani N, Kim GY, Suarez CJ, Chen JH. Applications of Machine Learning in Routine Laboratory Medicine: Current State and Future Directions. Clinical Biochemistry 2022. [DOI: 10.1016/j.clinbiochem.2022.02.011] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Kader R, Baggaley RF, Hussein M, Ahmad OF, Patel N, Corbett G, Dolwani S, Stoyanov D, Lovat LB. Survey on the perceptions of UK gastroenterologists and endoscopists to artificial intelligence. Frontline Gastroenterol. [DOI: 10.1136/flgastro-2021-101994] [Reference Citation Analysis]
19 Elfer K, Dudgeon S, Garcia V, Blenman K, Hytopoulos E, Wen S, Li X, Ly A, Werness B, Sheth MS, Amgad M, Gupta R, Saltz J, Hanna MG, Ehinger A, Peeters D, Salgado R, Gallas BD. Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms. J Med Imaging (Bellingham) 2022;9:047501. [PMID: 35911208 DOI: 10.1117/1.JMI.9.4.047501] [Reference Citation Analysis]
20 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]
21 Byeon SJ, Park J, Cho YA, Cho BJ. Automated histological classification for digital pathology images of colonoscopy specimen via deep learning. Sci Rep 2022;12:12804. [PMID: 35896791 DOI: 10.1038/s41598-022-16885-x] [Reference Citation Analysis]
22 Echle A, Laleh NG, Schrammen PL, West NP, Trautwein C, Brinker TJ, Gruber SB, Buelow RD, Boor P, Grabsch HI, Quirke P, Kather JN. Deep learning for the detection of microsatellite instability from histology images in colorectal cancer: A systematic literature review. ImmunoInformatics 2021;3-4:100008. [DOI: 10.1016/j.immuno.2021.100008] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
23 Boehm KM, Khosravi P, Vanguri R, Gao J, Shah SP. Harnessing multimodal data integration to advance precision oncology. Nat Rev Cancer 2021. [PMID: 34663944 DOI: 10.1038/s41568-021-00408-3] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
24 Fan J, Lee J, Lee Y. A Transfer Learning Architecture Based on a Support Vector Machine for Histopathology Image Classification. Applied Sciences 2021;11:6380. [DOI: 10.3390/app11146380] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 5.0] [Reference Citation Analysis]
25 Teranikar T, Lim J, Ijaseun T, Lee J. Development of Planar Illumination Strategies for Solving Mysteries in the Sub-Cellular Realm. Int J Mol Sci 2022;23:1643. [PMID: 35163562 DOI: 10.3390/ijms23031643] [Reference Citation Analysis]
26 Shi YC, Li J, Li SJ, Li ZP, Zhang HJ, Wu ZY, Wu ZY. Flap failure prediction in microvascular tissue reconstruction using machine learning algorithms. World J Clin Cases 2022; 10(12): 3729-3738 [DOI: 10.12998/wjcc.v10.i12.3729] [Reference Citation Analysis]
27 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]
28 Li H, Tsokos MG, Tsokos GC. Lymphocytes in the neighborhood: good or bad for the kidney? J Clin Invest 2022;132:e160657. [PMID: 35775489 DOI: 10.1172/JCI160657] [Reference Citation Analysis]
29 Fraggetta F, L'Imperio V, Ameisen D, Carvalho R, Leh S, Kiehl TR, Serbanescu M, Racoceanu D, Della Mea V, Polonia A, Zerbe N, Eloy C. Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP). Diagnostics (Basel) 2021;11:2167. [PMID: 34829514 DOI: 10.3390/diagnostics11112167] [Reference Citation Analysis]
30 Saheb T, Saheb T, Carpenter DO. Mapping research strands of ethics of artificial intelligence in healthcare: A bibliometric and content analysis. Comput Biol Med 2021;135:104660. [PMID: 34346319 DOI: 10.1016/j.compbiomed.2021.104660] [Reference Citation Analysis]
31 Li X, Cen M, Xu J, Zhang H, Xu XS. Improving feature extraction from histopathological images through a fine-tuning ImageNet model. Journal of Pathology Informatics 2022;13:100115. [DOI: 10.1016/j.jpi.2022.100115] [Reference Citation Analysis]
32 Trüb M, Zippelius A. Tertiary Lymphoid Structures as a Predictive Biomarker of Response to Cancer Immunotherapies. Front Immunol 2021;12:674565. [PMID: 34054861 DOI: 10.3389/fimmu.2021.674565] [Reference Citation Analysis]
33 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]
34 Evans T, Retzlaff CO, Geißler C, Kargl M, Plass M, Müller H, Kiehl T, Zerbe N, Holzinger A. The explainability paradox: Challenges for xAI in digital pathology. Future Generation Computer Systems 2022. [DOI: 10.1016/j.future.2022.03.009] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
35 Chang S, Sadimin E, Yao K, Hamilton S, Aoun P, Pillai R, Muirhead D, Schmolze D. Establishment of a whole slide imaging-based frozen section service at a cancer center. Journal of Pathology Informatics 2022. [DOI: 10.1016/j.jpi.2022.100106] [Reference Citation Analysis]
36 Alrafiah AR. Application and performance of artificial intelligence technology in cytopathology. Acta Histochem 2022;124:151890. [PMID: 35366580 DOI: 10.1016/j.acthis.2022.151890] [Reference Citation Analysis]
37 Ghezloo F, Wang P, Kerr KF, Brunyé TT, Drew T, Chang OH, Reisch LM, Shapiro LG, Elmore JG. An Analysis of Pathologists’ Viewing Processes as They Diagnose Whole Slide Digital Images. Journal of Pathology Informatics 2022. [DOI: 10.1016/j.jpi.2022.100104] [Reference Citation Analysis]
38 Drogt J, Milota M, Vos S, Bredenoord A, Jongsma K. Integrating artificial intelligence in pathology: a qualitative interview study of users' experiences and expectations. Mod Pathol 2022. [PMID: 35927490 DOI: 10.1038/s41379-022-01123-6] [Reference Citation Analysis]
39 Dika E, Curti N, Giampieri E, Veronesi G, Misciali C, Ricci C, Castellani G, Patrizi A, Marcelli E. Advantages of manual and automatic computer-aided compared to traditional histopathological diagnosis of melanoma: A pilot study. Pathol Res Pract 2022;237:154014. [PMID: 35870238 DOI: 10.1016/j.prp.2022.154014] [Reference Citation Analysis]
40 Orsulic S, John J, Walts AE, Gertych A. Computational pathology in ovarian cancer. Front Oncol 2022;12:924945. [DOI: 10.3389/fonc.2022.924945] [Reference Citation Analysis]
41 Melo PAS, Estivallet CLN, Srougi M, Nahas WC, Leite KRM. Detecting and grading prostate cancer in radical prostatectomy specimens through deep learning techniques. Clinics (Sao Paulo) 2021;76:e3198. [PMID: 34730614 DOI: 10.6061/clinics/2021/e3198] [Reference Citation Analysis]
42 Morrison LE, Lefever MR, Lewis HN, Kapadia MJ, Bauer DR. Conventional histological and cytological staining with simultaneous immunohistochemistry enabled by invisible chromogens. Lab Invest 2021. [PMID: 34963687 DOI: 10.1038/s41374-021-00714-2] [Reference Citation Analysis]
43 Laudus N, Nijs L, Nauwelaers I, Dequeker EMC. The Significance of External Quality Assessment Schemes for Molecular Testing in Clinical Laboratories. Cancers 2022;14:3686. [DOI: 10.3390/cancers14153686] [Reference Citation Analysis]