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For: Bychkov D, Linder N, Tiulpin A, Kücükel H, Lundin M, Nordling S, Sihto H, Isola J, Lehtimäki T, Kellokumpu-Lehtinen PL, von Smitten K, Joensuu H, Lundin J. Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy. Sci Rep 2021;11:4037. [PMID: 33597560 DOI: 10.1038/s41598-021-83102-6] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Pourasad Y, Zarouri E, Salemizadeh Parizi M, Salih Mohammed A. Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning. Diagnostics (Basel) 2021;11:1870. [PMID: 34679568 DOI: 10.3390/diagnostics11101870] [Reference Citation Analysis]
2 Laleh NG, Muti HS, Loeffler CML, Echle A, Saldanha OL, Mahmood F, Lu MY, Trautwein C, Langer R, Dislich B, Buelow RD, Grabsch HI, Brenner H, Chang-claude J, Alwers E, Brinker TJ, Khader F, Truhn D, Gaisa NT, Boor P, Hoffmeister M, Schulz V, Kather JN. Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102474] [Reference Citation Analysis]
3 Elemento O, Leslie C, Lundin J, Tourassi G. Artificial intelligence in cancer research, diagnosis and therapy. Nat Rev Cancer 2021. [PMID: 34535775 DOI: 10.1038/s41568-021-00399-1] [Reference Citation Analysis]
4 Laurinavicius A, Rasmusson A, Plancoulaine B, Shribak M, Levenson R. Machine-Learning-Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance. Am J Pathol 2021:S0002-9440(21)00165-6. [PMID: 33895120 DOI: 10.1016/j.ajpath.2021.04.008] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Schrammen PL, Ghaffari Laleh N, Echle A, Truhn D, Schulz V, Brinker TJ, Brenner H, Chang-Claude J, Alwers E, Brobeil A, Kloor M, Heij LR, Jäger D, Trautwein C, Grabsch HI, Quirke P, West NP, Hoffmeister M, Kather JN. Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology. J Pathol 2021. [PMID: 34561876 DOI: 10.1002/path.5800] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Chen SB, Novoa RA. Artificial intelligence for dermatopathology: Current trends and the road ahead. Seminars in Diagnostic Pathology 2022. [DOI: 10.1053/j.semdp.2022.01.003] [Reference Citation Analysis]
7 Garberis I, Andre F, Lacroix-Triki M. L’intelligence artificielle pourrait-elle intervenir dans l’aide au diagnostic des cancers du sein ? – L’exemple de HER2: Could artificial intelligence play a role in breast cancer diagnosis? – The example of HER2. Bull Cancer 2021;108:11S35-45. [PMID: 34969514 DOI: 10.1016/S0007-4551(21)00635-4] [Reference Citation Analysis]
8 Zagidullin B, Wang Z, Guan Y, Pitkänen E, Tang J. Comparative analysis of molecular fingerprints in prediction of drug combination effects. Brief Bioinform 2021;22:bbab291. [PMID: 34401895 DOI: 10.1093/bib/bbab291] [Reference Citation Analysis]
9 Farahmand S, Fernandez AI, Ahmed FS, Rimm DL, Chuang JH, Reisenbichler E, Zarringhalam K. Deep learning trained on hematoxylin and eosin tumor region of Interest predicts HER2 status and trastuzumab treatment response in HER2+ breast cancer. Mod Pathol 2021. [PMID: 34493825 DOI: 10.1038/s41379-021-00911-w] [Reference Citation Analysis]