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For: Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, Brogi E, Reuter VE, Klimstra DS, Fuchs TJ. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019;25:1301-1309. [PMID: 31308507 DOI: 10.1038/s41591-019-0508-1] [Cited by in Crossref: 353] [Cited by in F6Publishing: 279] [Article Influence: 117.7] [Reference Citation Analysis]
Number Citing Articles
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8 Liu MZ, Swintelski C, Sun S, Siddique M, Desperito E, Jambawalikar S, Ha R. Weakly Supervised Deep Learning Approach to Breast MRI Assessment. Acad Radiol 2021:S1076-6332(21)00202-6. [PMID: 34108114 DOI: 10.1016/j.acra.2021.03.032] [Reference Citation Analysis]
9 Wang X, Fang Y, Yang S, Zhu D, Wang M, Zhang J, Tong KY, Han X. A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images. Med Image Anal. 2021;68:101914. [PMID: 33285479 DOI: 10.1016/j.media.2020.101914] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
10 Woerl AC, Eckstein M, Geiger J, Wagner DC, Daher T, Stenzel P, Fernandez A, Hartmann A, Wand M, Roth W, Foersch S. Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides. Eur Urol 2020;78:256-64. [PMID: 32354610 DOI: 10.1016/j.eururo.2020.04.023] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 5.5] [Reference Citation Analysis]
11 Pagni F, Malapelle U, Doglioni C, Fontanini G, Fraggetta F, Graziano P, Marchetti A, Guerini Rocco E, Pisapia P, Vigliar EV, Buttitta F, Jaconi M, Fusco N, Barberis M, Troncone G. Digital Pathology and PD-L1 Testing in Non Small Cell Lung Cancer: A Workshop Record. Cancers (Basel) 2020;12:E1800. [PMID: 32635634 DOI: 10.3390/cancers12071800] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
12 Tanaka R, Fujiwara M, Sakamoto N, Suzuki H, Tachibana K, Ohtsuka K, Kishimoto K, Kamma H, Shibahara J, Kondo H. Cytomorphometric and flow cytometric analyses using liquid-based cytology materials in subtypes of lung adenocarcinoma. Diagn Cytopathol 2022. [PMID: 35567786 DOI: 10.1002/dc.24978] [Reference Citation Analysis]
13 Kimeswenger S, Tschandl P, Noack P, Hofmarcher M, Rumetshofer E, Kindermann H, Silye R, Hochreiter S, Kaltenbrunner M, Guenova E, Klambauer G, Hoetzenecker W. Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns. Mod Pathol 2021;34:895-903. [PMID: 33184470 DOI: 10.1038/s41379-020-00712-7] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
14 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]
15 Kalra S, Tizhoosh HR, Shah S, Choi C, Damaskinos S, Safarpoor A, Shafiei S, Babaie M, Diamandis P, Campbell CJV, Pantanowitz L. Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence. NPJ Digit Med 2020;3:31. [PMID: 32195366 DOI: 10.1038/s41746-020-0238-2] [Cited by in Crossref: 21] [Cited by in F6Publishing: 9] [Article Influence: 10.5] [Reference Citation Analysis]
16 Polesie S, McKee PH, Gardner JM, Gillstedt M, Siarov J, Neittaanmäki N, Paoli J. Attitudes Toward Artificial Intelligence Within Dermatopathology: An International Online Survey. Front Med (Lausanne) 2020;7:591952. [PMID: 33195357 DOI: 10.3389/fmed.2020.591952] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
17 Yao J, Zhu X, Jonnagaddala J, Hawkins N, Huang J. Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks. Medical Image Analysis 2020;65:101789. [DOI: 10.1016/j.media.2020.101789] [Cited by in Crossref: 17] [Cited by in F6Publishing: 4] [Article Influence: 8.5] [Reference Citation Analysis]
18 Bulten W, Pinckaers H, van Boven H, Vink R, de Bel T, van Ginneken B, van der Laak J, Hulsbergen-van de Kaa C, Litjens G. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol 2020;21:233-41. [PMID: 31926805 DOI: 10.1016/S1470-2045(19)30739-9] [Cited by in Crossref: 105] [Cited by in F6Publishing: 46] [Article Influence: 52.5] [Reference Citation Analysis]
19 Abel JH, Badgeley MA, Meschede-Krasa B, Schamberg G, Garwood IC, Lecamwasam K, Chakravarty S, Zhou DW, Keating M, Purdon PL, Brown EN. Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia. PLoS One 2021;16:e0246165. [PMID: 33956800 DOI: 10.1371/journal.pone.0246165] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Li D, Bledsoe JR, Zeng Y, Liu W, Hu Y, Bi K, Liang A, Li S. A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals. Nat Commun 2020;11:6004. [PMID: 33244018 DOI: 10.1038/s41467-020-19817-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
21 Rakha EA, Toss M, Shiino S, Gamble P, Jaroensri R, Mermel CH, Chen PC. Current and future applications of artificial intelligence in pathology: a clinical perspective. J Clin Pathol 2021;74:409-14. [PMID: 32763920 DOI: 10.1136/jclinpath-2020-206908] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
22 Park J, Chung YR, Kong ST, Kim YW, Park H, Kim K, Kim DI, Jung KH. Aggregation of cohorts for histopathological diagnosis with deep morphological analysis. Sci Rep 2021;11:2876. [PMID: 33536550 DOI: 10.1038/s41598-021-82642-1] [Reference Citation Analysis]
23 Gao R, Huo Y, Bao S, Tang Y, Antic SL, Epstein ES, Deppen S, Paulson AB, Sandler KL, Massion PP, Landman BA. Multi-path x-D Recurrent Neural Networks for Collaborative Image Classification. Neurocomputing 2020;397:48-59. [PMID: 32863584 DOI: 10.1016/j.neucom.2020.02.033] [Cited by in Crossref: 5] [Article Influence: 2.5] [Reference Citation Analysis]
24 Bukowy JD, Foss H, McGarry SD, Lowman AK, Hurrell SL, Iczkowski KA, Banerjee A, Bobholz SA, Barrington A, Dayton A, Unteriner J, Jacobsohn K, See WA, Nevalainen MT, Nencka AS, Ethridge T, Jarrard DF, LaViolette PS. Accurate segmentation of prostate cancer histomorphometric features using a weakly supervised convolutional neural network. J Med Imaging (Bellingham) 2020;7:057501. [PMID: 33062803 DOI: 10.1117/1.JMI.7.5.057501] [Reference Citation Analysis]
25 Yoshida H, Kiyuna T. Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology. World J Gastroenterol 2021; 27(21): 2818-2833 [PMID: 34135556 DOI: 10.3748/wjg.v27.i21.2818] [Reference Citation Analysis]
26 Schaumberg AJ, Juarez-Nicanor WC, Choudhury SJ, Pastrián LG, Pritt BS, Prieto Pozuelo M, Sotillo Sánchez R, Ho K, Zahra N, Sener BD, Yip S, Xu B, Annavarapu SR, Morini A, Jones KA, Rosado-Orozco K, Mukhopadhyay S, Miguel C, Yang H, Rosen Y, Ali RH, Folaranmi OO, Gardner JM, Rusu C, Stayerman C, Gross J, Suleiman DE, Sirintrapun SJ, Aly M, Fuchs TJ. Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media. Mod Pathol 2020;33:2169-85. [PMID: 32467650 DOI: 10.1038/s41379-020-0540-1] [Cited by in Crossref: 11] [Cited by in F6Publishing: 5] [Article Influence: 5.5] [Reference Citation Analysis]
27 Roohi A, Faust K, Djuric U, Diamandis P. Unsupervised Machine Learning in Pathology: The Next Frontier. Surg Pathol Clin 2020;13:349-58. [PMID: 32389272 DOI: 10.1016/j.path.2020.01.002] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 3.5] [Reference Citation Analysis]
28 Tavolara TE, Niazi MKK, Ginese M, Piedra-Mora C, Gatti DM, Beamer G, Gurcan MN. Automatic discovery of clinically interpretable imaging biomarkers for Mycobacterium tuberculosis supersusceptibility using deep learning. EBioMedicine 2020;62:103094. [PMID: 33166789 DOI: 10.1016/j.ebiom.2020.103094] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
29 Cheng J, Kuang H, Zhao Q, Wang Y, Xu L, Liu J, Wang J. DWT-CV: Dense weight transfer-based cross validation strategy for model selection in biomedical data analysis. Future Generation Computer Systems 2022. [DOI: 10.1016/j.future.2022.04.025] [Reference Citation Analysis]
30 Levine AB, Peng J, Farnell D, Nursey M, Wang Y, Naso JR, Ren H, Farahani H, Chen C, Chiu D, Talhouk A, Sheffield B, Riazy M, Ip PP, Parra-Herran C, Mills A, Singh N, Tessier-Cloutier B, Salisbury T, Lee J, Salcudean T, Jones SJ, Huntsman DG, Gilks CB, Yip S, Bashashati A. Synthesis of diagnostic quality cancer pathology images by generative adversarial networks. J Pathol 2020;252:178-88. [PMID: 32686118 DOI: 10.1002/path.5509] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
31 Oliveira SP, Neto PC, Fraga J, Montezuma D, Monteiro A, Monteiro J, Ribeiro L, Gonçalves S, Pinto IM, Cardoso JS. CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance. Sci Rep 2021;11:14358. [PMID: 34257363 DOI: 10.1038/s41598-021-93746-z] [Reference Citation Analysis]
32 Liu H, Zhao Y, Yang F, Lou X, Wu F, Li H, Xing X, Peng T, Menze B, Huang J, Zhang S, Han A, Yao J, Fan X. Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer with Deep Learning. BME Frontiers 2022;2022:1-12. [DOI: 10.34133/2022/9860179] [Reference Citation Analysis]
33 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]
34 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]
35 Yang Y, Lure FYM, Miao H, Zhang Z, Jaeger S, Liu J, Guo L. Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections. J Xray Sci Technol 2021;29:1-17. [PMID: 33164982 DOI: 10.3233/XST-200735] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
36 Xu Z, Li X, Zhu X, Chen L, He Y, Chen Y. Effective Immunohistochemistry Pathology Microscopy Image Generation Using CycleGAN. Front Mol Biosci 2020;7:571180. [PMID: 33195418 DOI: 10.3389/fmolb.2020.571180] [Reference Citation Analysis]
37 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]
38 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]
39 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]
40 Tian Y, Fu S. A descriptive framework for the field of deep learning applications in medical images. Knowledge-Based Systems 2020;210:106445. [DOI: 10.1016/j.knosys.2020.106445] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
41 Jayapandian CP, Chen Y, Janowczyk AR, Palmer MB, Cassol CA, Sekulic M, Hodgin JB, Zee J, Hewitt SM, O'Toole J, Toro P, Sedor JR, Barisoni L, Madabhushi A; Nephrotic Syndrome Study Network (NEPTUNE). Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains. Kidney Int 2021;99:86-101. [PMID: 32835732 DOI: 10.1016/j.kint.2020.07.044] [Cited by in Crossref: 14] [Cited by in F6Publishing: 6] [Article Influence: 7.0] [Reference Citation Analysis]
42 Truong AH, Sharmanska V, Limbӓck-Stanic C, Grech-Sollars M. Optimization of deep learning methods for visualization of tumor heterogeneity and brain tumor grading through digital pathology. Neurooncol Adv 2020;2:vdaa110. [PMID: 33196039 DOI: 10.1093/noajnl/vdaa110] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
43 Verzat C, Harley J, Patani R, Luisier R. Image-based deep learning reveals the responses of human motor neurons to stress and VCP-related ALS. Neuropathol Appl Neurobiol 2021. [PMID: 34595747 DOI: 10.1111/nan.12770] [Reference Citation Analysis]
44 Loftus TJ, Shickel B, Ozrazgat-Baslanti T, Ren Y, Glicksberg BS, Cao J, Singh K, Chan L, Nadkarni GN, Bihorac A. Artificial intelligence-enabled decision support in nephrology. Nat Rev Nephrol 2022. [PMID: 35459850 DOI: 10.1038/s41581-022-00562-3] [Reference Citation Analysis]
45 D'Alonzo M, Bozkurt A, Alessi-Fox C, Gill M, Brooks DH, Rajadhyaksha M, Kose K, Dy JG. Semantic segmentation of reflectance confocal microscopy mosaics of pigmented lesions using weak labels. Sci Rep 2021;11:3679. [PMID: 33574486 DOI: 10.1038/s41598-021-82969-9] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
46 Brinker TJ, Kiehl L, Schmitt M, Jutzi TB, Krieghoff-Henning EI, Krahl D, Kutzner H, Gholam P, Haferkamp S, Klode J, Schadendorf D, Hekler A, Fröhling S, Kather JN, Haggenmüller S, von Kalle C, Heppt M, Hilke F, Ghoreschi K, Tiemann M, Wehkamp U, Hauschild A, Weichenthal M, Utikal JS. Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours. Eur J Cancer 2021;154:227-34. [PMID: 34298373 DOI: 10.1016/j.ejca.2021.05.026] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
47 Dov D, Kovalsky SZ, Assaad S, Cohen J, Range DE, Pendse AA, Henao R, Carin L. Weakly supervised instance learning for thyroid malignancy prediction from whole slide cytopathology images. Med Image Anal 2021;67:101814. [PMID: 33049578 DOI: 10.1016/j.media.2020.101814] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
48 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]
49 Fu Y, Jung AW, Torne RV, Gonzalez S, Vöhringer H, Shmatko A, Yates LR, Jimenez-linan M, Moore L, Gerstung M. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat Cancer 2020;1:800-10. [DOI: 10.1038/s43018-020-0085-8] [Cited by in Crossref: 55] [Cited by in F6Publishing: 19] [Article Influence: 27.5] [Reference Citation Analysis]
50 Shi JY, Wang X, Ding GY, Dong Z, Han J, Guan Z, Ma LJ, Zheng Y, Zhang L, Yu GZ, Wang XY, Ding ZB, Ke AW, Yang H, Wang L, Ai L, Cao Y, Zhou J, Fan J, Liu X, Gao Q. Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning. Gut 2021;70:951-61. [PMID: 32998878 DOI: 10.1136/gutjnl-2020-320930] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
51 Bao Y, Zhang J, Zhao X, Zhou H, Chen Y, Jian J, Shi T, Gao X. Deep Learning-Based Fully Automated Diagnosis of Melanocytic Lesions by Using Whole Slide Images. J Dermatolog Treat 2022;:1-31. [PMID: 35112978 DOI: 10.1080/09546634.2022.2038772] [Reference Citation Analysis]
52 Chikontwe P, Luna M, Kang M, Hong KS, Ahn JH, Park SH. Dual attention multiple instance learning with unsupervised complementary loss for COVID-19 screening. Med Image Anal 2021;72:102105. [PMID: 34102477 DOI: 10.1016/j.media.2021.102105] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
53 Chuang WY, Chen CC, Yu WH, Yeh CJ, Chang SH, Ueng SH, Wang TH, Hsueh C, Kuo CF, Yeh CY. Identification of nodal micrometastasis in colorectal cancer using deep learning on annotation-free whole-slide images. Mod Pathol 2021. [PMID: 34103664 DOI: 10.1038/s41379-021-00838-2] [Reference Citation Analysis]
54 Stenzinger A, Alber M, Allgäuer M, Jurmeister P, Bockmayr M, Budczies J, Lennerz J, Eschrich J, Kazdal D, Schirmacher P, Wagner AH, Tacke F, Capper D, Müller KR, Klauschen F. Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling. Semin Cancer Biol 2021:S1044-579X(21)00034-1. [PMID: 33631297 DOI: 10.1016/j.semcancer.2021.02.011] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
55 Tufail AB, Ma YK, Kaabar MKA, Martínez F, Junejo AR, Ullah I, Khan R. Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions. Comput Math Methods Med 2021;2021:9025470. [PMID: 34754327 DOI: 10.1155/2021/9025470] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
56 Guo YN, Tian DP, Gong QY, Huang H, Yang P, Chen SB, Billan S, He JY, Huang HH, Xiong P, Lin WT, Guo D, Marom M, Gil Z, Su M. Perineural Invasion is a Better Prognostic Indicator than Lymphovascular Invasion and a Potential Adjuvant Therapy Indicator for pN0M0 Esophageal Squamous Cell Carcinoma. Ann Surg Oncol 2020;27:4371-81. [PMID: 32519146 DOI: 10.1245/s10434-020-08667-4] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.5] [Reference Citation Analysis]
57 Saldanha OL, Quirke P, West NP, James JA, Loughrey MB, Grabsch HI, Salto-Tellez M, Alwers E, Cifci D, Ghaffari Laleh N, Seibel T, Gray R, Hutchins GGA, Brenner H, van Treeck M, Yuan T, Brinker TJ, Chang-Claude J, Khader F, Schuppert A, Luedde T, Trautwein C, Muti HS, Foersch S, Hoffmeister M, Truhn D, Kather JN. Swarm learning for decentralized artificial intelligence in cancer histopathology. Nat Med 2022. [PMID: 35469069 DOI: 10.1038/s41591-022-01768-5] [Reference Citation Analysis]
58 Hanna MG, Parwani A, Sirintrapun SJ. Whole Slide Imaging: Technology and Applications. Adv Anat Pathol. 2020;27:251-259. [PMID: 32452840 DOI: 10.1097/pap.0000000000000273] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
59 Jin W, Luo Q. When artificial intelligence meets PD-1/PD-L1 inhibitors: Population screening, response prediction and efficacy evaluation. Computers in Biology and Medicine 2022;145:105499. [DOI: 10.1016/j.compbiomed.2022.105499] [Reference Citation Analysis]
60 Liu JTC, Glaser AK, Bera K, True LD, Reder NP, Eliceiri KW, Madabhushi A. Harnessing non-destructive 3D pathology. Nat Biomed Eng 2021;5:203-18. [PMID: 33589781 DOI: 10.1038/s41551-020-00681-x] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
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