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For: Lu MY, Williamson DFK, Chen TY, Chen RJ, Barbieri M, Mahmood F. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat Biomed Eng 2021;5:555-70. [PMID: 33649564 DOI: 10.1038/s41551-020-00682-w] [Cited by in Crossref: 9] [Cited by in F6Publishing: 12] [Article Influence: 9.0] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 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]
3 Lipkova J, Chen TY, Lu MY, Chen RJ, Shady M, Williams M, Wang J, Noor Z, Mitchell RN, Turan M, Coskun G, Yilmaz F, Demir D, Nart D, Basak K, Turhan N, Ozkara S, Banz Y, Odening KE, Mahmood F. Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies. Nat Med 2022;28:575-82. [PMID: 35314822 DOI: 10.1038/s41591-022-01709-2] [Reference Citation Analysis]
4 Zhang J, Hua Z, Yan K, Tian K, Yao J, Liu E, Liu M, Han X. Joint fully convolutional and graph convolutional networks for weakly-supervised segmentation of pathology images. Med Image Anal 2021;73:102183. [PMID: 34340108 DOI: 10.1016/j.media.2021.102183] [Reference Citation Analysis]
5 Shaban M, Raza SEA, Hassan M, Jamshed A, Mushtaq S, Loya A, Batis N, Brooks J, Nankivell P, Sharma N, Robinson M, Mehanna H, Khurram SA, Rajpoot N. A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma. J Pathol 2021. [PMID: 34698394 DOI: 10.1002/path.5819] [Reference Citation Analysis]
6 Mehta S, Lu X, Wu W, Weaver D, Hajishirzi H, Elmore JG, Shapiro LG. End-to-End Diagnosis of Breast Biopsy Images with Transformers. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102466] [Reference Citation Analysis]
7 Abbet C, Studer L, Fischer A, Dawson H, Zlobec I, Bozorgtabar B, Thiran J. Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102473] [Reference Citation Analysis]
8 Li J, Li W, Sisk A, Ye H, Wallace WD, Speier W, Arnold CW. A multi-resolution model for histopathology image classification and localization with multiple instance learning. Comput Biol Med 2021;131:104253. [PMID: 33601084 DOI: 10.1016/j.compbiomed.2021.104253] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Veta M, van Diest PJ, Vink A. Can automatic image analysis replace the pathologist in cardiac allograft rejection diagnosis? Eur Heart J 2021;42:2370-2. [PMID: 34000014 DOI: 10.1093/eurheartj/ehab226] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
10 Mun Y, Paik I, Shin SJ, Kwak TY, Chang H. Yet Another Automated Gleason Grading System (YAAGGS) by weakly supervised deep learning. NPJ Digit Med 2021;4:99. [PMID: 34127777 DOI: 10.1038/s41746-021-00469-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Med 2021;13:152. [PMID: 34579788 DOI: 10.1186/s13073-021-00968-x] [Reference Citation Analysis]
12 Lu MY, Chen RJ, Kong D, Lipkova J, Singh R, Williamson DFK, Chen TY, Mahmood F. Federated learning for computational pathology on gigapixel whole slide images. Med Image Anal 2021;76:102298. [PMID: 34911013 DOI: 10.1016/j.media.2021.102298] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Su Z, Tavolara TE, Carreno-galeano G, Lee SJ, Gurcan MN, Niazi M. Attention2majority: Weak multiple instance learning for regenerative kidney grading on whole slide images. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102462] [Reference Citation Analysis]
14 Zeng Q, Klein C, Caruso S, Maille P, Laleh NG, Sommacale D, Laurent A, Amaddeo G, Gentien D, Rapinat A, Regnault H, Charpy C, Nguyen CT, Tournigand C, Brustia R, Pawlotsky JM, Kather JN, Maiuri MC, Loménie N, Calderaro J. Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology. J Hepatol 2022:S0168-8278(22)00031-9. [PMID: 35143898 DOI: 10.1016/j.jhep.2022.01.018] [Reference Citation Analysis]
15 Zhu M, Ren B, Richards R, Suriawinata M, Tomita N, Hassanpour S. Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides. Sci Rep 2021;11:7080. [PMID: 33782535 DOI: 10.1038/s41598-021-86540-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
16 Lu MY, Chen TY, Williamson DFK, Zhao M, Shady M, Lipkova J, Mahmood F. AI-based pathology predicts origins for cancers of unknown primary. Nature 2021;594:106-10. [PMID: 33953404 DOI: 10.1038/s41586-021-03512-4] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 9.0] [Reference Citation Analysis]
17 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]
18 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]
19 Xue YF, He Y, Wang J, Ren KF, Tian P, Ji J. Label-Free and In Situ Identification of Cells via Combinational Machine Learning Models. Small Methods 2021;:e2101405. [PMID: 34954897 DOI: 10.1002/smtd.202101405] [Reference Citation Analysis]
20 Zhao L, Xu X, Hou R, Zhao W, Zhong H, Teng H, Han Y, Fu X, Sun J, Zhao J. Lung cancer subtype classification using histopathological images based on weakly supervised multi-instance learning. Phys Med Biol 2021;66. [PMID: 34794136 DOI: 10.1088/1361-6560/ac3b32] [Reference Citation Analysis]
21 Kuklyte J, Fitzgerald J, Nelissen S, Wei H, Whelan A, Power A, Ahmad A, Miarka M, Gregson M, Maxwell M, Raji R, Lenihan J, Finn-Moloney E, Rafferty M, Cary M, Barale-Thomas E, O'Shea D. Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies. Toxicol Pathol 2021;49:815-42. [PMID: 33618634 DOI: 10.1177/0192623320986423] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Dang VN, Galati F, Cortese R, Di Giacomo G, Marconetto V, Mathur P, Lekadir K, Lorenzi M, Prados F, Zuluaga MA. Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation. Med Image Anal 2021;75:102263. [PMID: 34731770 DOI: 10.1016/j.media.2021.102263] [Reference Citation Analysis]
23 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]
24 Wang Q, Zou Y, Zhang J, Liu B. Second-order multi-instance learning model for whole slide image classification. Phys Med Biol 2021;66. [PMID: 34181583 DOI: 10.1088/1361-6560/ac0f30] [Reference Citation Analysis]
25 Bilal M, Raza SEA, Azam A, Graham S, Ilyas M, Cree IA, Snead D, Minhas F, Rajpoot NM. Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study. Lancet Digit Health 2021;3:e763-72. [PMID: 34686474 DOI: 10.1016/S2589-7500(21)00180-1] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]