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For: Madabhushi A, Lee G. Image analysis and machine learning in digital pathology: Challenges and opportunities. Med Image Anal. 2016;33:170-175. [PMID: 27423409 DOI: 10.1016/j.media.2016.06.037] [Cited by in Crossref: 324] [Cited by in F6Publishing: 247] [Article Influence: 54.0] [Reference Citation Analysis]
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5 Tizhoosh HR, Pantanowitz L. Artificial Intelligence and Digital Pathology: Challenges and Opportunities. J Pathol Inform 2018;9:38. [PMID: 30607305 DOI: 10.4103/jpi.jpi_53_18] [Cited by in Crossref: 113] [Cited by in F6Publishing: 87] [Article Influence: 28.3] [Reference Citation Analysis]
6 Salviato T, Bonetti LR, Mangogna A, Leoncini G, Cadei M, Caprioli F, Armuzzi A, Daperno M, Villanacci V. Microscopic imaging of Inflammatory Bowel Disease (IBD) and Non-IBD Colitis on digital slides: The Italian Group-IBD Pathologists experience. Pathol Res Pract 2020;216:153189. [PMID: 32906010 DOI: 10.1016/j.prp.2020.153189] [Reference Citation Analysis]
7 Mariani LH, Bomback AS, Canetta PA, Flessner MF, Helmuth M, Hladunewich MA, Hogan JJ, Kiryluk K, Nachman PH, Nast CC, Rheault MN, Rizk DV, Trachtman H, Wenderfer SE, Bowers C, Hill-Callahan P, Marasa M, Poulton CJ, Revell A, Vento S, Barisoni L, Cattran D, D'Agati V, Jennette JC, Klein JB, Laurin LP, Twombley K, Falk RJ, Gharavi AG, Gillespie BW, Gipson DS, Greenbaum LA, Holzman LB, Kretzler M, Robinson B, Smoyer WE, Guay-Woodford LM; CureGN Consortium. CureGN Study Rationale, Design, and Methods: Establishing a Large Prospective Observational Study of Glomerular Disease. Am J Kidney Dis 2019;73:218-29. [PMID: 30420158 DOI: 10.1053/j.ajkd.2018.07.020] [Cited by in Crossref: 29] [Cited by in F6Publishing: 24] [Article Influence: 7.3] [Reference Citation Analysis]
8 Scodellaro R, Bouzin M, Mingozzi F, D'Alfonso L, Granucci F, Collini M, Chirico G, Sironi L. Whole-Section Tumor Micro-Architecture Analysis by a Two-Dimensional Phasor-Based Approach Applied to Polarization-Dependent Second Harmonic Imaging. Front Oncol 2019;9:527. [PMID: 31275857 DOI: 10.3389/fonc.2019.00527] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 1.7] [Reference Citation Analysis]
9 Barsch F, Mamilos A, Babel M, Wagner WL, Winther HB, Schmitt VH, Hierlemann H, Teufel A, Brochhausen C. Semiautomated quantification of the fibrous tissue response to complex three-dimensional filamentous scaffolds using digital image analysis. J Biomed Mater Res A 2021. [PMID: 34390322 DOI: 10.1002/jbm.a.37293] [Reference Citation Analysis]
10 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]
11 Hahn A, Podbielski A, Meyer T, Zautner AE, Loderstädt U, Schwarz NG, Krüger A, Cadar D, Frickmann H. On detection thresholds-a review on diagnostic approaches in the infectious disease laboratory and the interpretation of their results. Acta Trop 2020;205:105377. [PMID: 32007448 DOI: 10.1016/j.actatropica.2020.105377] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 6.0] [Reference Citation Analysis]
12 Chen L, Zeng H, Xiang Y, Huang Y, Luo Y, Ma X. Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma. Front Cell Dev Biol 2021;9:720110. [PMID: 34708036 DOI: 10.3389/fcell.2021.720110] [Reference Citation Analysis]
13 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]
14 Chen Y, Li T, Zhang Q, Mao W, Guan N, Tian M, Yu H, Zhuo C. ANT-UNet: Accurate and Noise-Tolerant Segmentation for Pathology Image Processing. J Emerg Technol Comput Syst 2022;18:1-17. [DOI: 10.1145/3451213] [Reference Citation Analysis]
15 Takabayashi M, Majeed H, Kajdacsy-Balla A, Popescu G. Tissue spatial correlation as cancer marker. J Biomed Opt 2019;24:1-6. [PMID: 30666854 DOI: 10.1117/1.JBO.24.1.016502] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
16 Wan T, Zhao L, Feng H, Li D, Tong C, Qin Z. Robust nuclei segmentation in histopathology using ASPPU-Net and boundary refinement. Neurocomputing 2020;408:144-56. [DOI: 10.1016/j.neucom.2019.08.103] [Cited by in Crossref: 7] [Cited by in F6Publishing: 2] [Article Influence: 3.5] [Reference Citation Analysis]
17 Ali MAS, Misko O, Salumaa SO, Papkov M, Palo K, Fishman D, Parts L. Evaluating Very Deep Convolutional Neural Networks for Nucleus Segmentation from Brightfield Cell Microscopy Images. SLAS Discov 2021;:24725552211023214. [PMID: 34167359 DOI: 10.1177/24725552211023214] [Reference Citation Analysis]
18 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]
19 Park Y, Depeursinge C, Popescu G. Quantitative phase imaging in biomedicine. Nature Photon 2018;12:578-89. [DOI: 10.1038/s41566-018-0253-x] [Cited by in Crossref: 380] [Cited by in F6Publishing: 94] [Article Influence: 95.0] [Reference Citation Analysis]
20 Ščupáková K, Dewez F, Walch AK, Heeren RMA, Balluff B. Morphometric Cell Classification for Single‐Cell MALDI‐Mass Spectrometry Imaging. Angew Chem 2020;132:17600-3. [DOI: 10.1002/ange.202007315] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
21 Al-thelaya K, Agus M, Gilal NU, Yang Y, Pintore G, Gobbetti E, Calí C, Magistretti PJ, Mifsud W, Schneider J. InShaDe: Invariant Shape Descriptors for visual 2D and 3D cellular and nuclear shape analysis and classification. Computers & Graphics 2021;98:105-25. [DOI: 10.1016/j.cag.2021.04.037] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
22 Sorell T, Rajpoot N, Verrill C. Ethical issues in computational pathology. J Med Ethics 2021:medethics-2020-107024. [PMID: 33658334 DOI: 10.1136/medethics-2020-107024] [Reference Citation Analysis]
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24 Korbar B, Olofson AM, Miraflor AP, Nicka CM, Suriawinata MA, Torresani L, Suriawinata AA, Hassanpour S. Deep Learning for Classification of Colorectal Polyps on Whole-slide Images. J Pathol Inform. 2017;8:30. [PMID: 28828201 DOI: 10.4103/jpi.jpi_34_17] [Cited by in Crossref: 84] [Cited by in F6Publishing: 65] [Article Influence: 16.8] [Reference Citation Analysis]
25 Downing MJ, Papke DJ Jr, Tyekucheva S, Mutter GL. A New Classification of Benign, Premalignant, and Malignant Endometrial Tissues Using Machine Learning Applied to 1413 Candidate Variables. Int J Gynecol Pathol 2020;39:333-43. [PMID: 31157686 DOI: 10.1097/PGP.0000000000000615] [Cited by in Crossref: 5] [Article Influence: 5.0] [Reference Citation Analysis]
26 Khan UAH, Stürenberg C, Gencoglu O, Sandeman K, Heikkinen T, Rannikko A, Mirtti T. Improving Prostate Cancer Detection with Breast Histopathology Images. In: Reyes-aldasoro CC, Janowczyk A, Veta M, Bankhead P, Sirinukunwattana K, editors. Digital Pathology. Cham: Springer International Publishing; 2019. pp. 91-9. [DOI: 10.1007/978-3-030-23937-4_11] [Cited by in Crossref: 7] [Cited by in F6Publishing: 1] [Article Influence: 2.3] [Reference Citation Analysis]
27 Kutzner H, Jutzi TB, Krahl D, Krieghoff-Henning EI, Heppt MV, Hekler A, Schmitt M, Maron RCR, Fröhling S, von Kalle C, Brinker TJ. Overdiagnosis of melanoma - causes, consequences and solutions. J Dtsch Dermatol Ges 2020;18:1236-43. [PMID: 32841508 DOI: 10.1111/ddg.14233] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 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]
29 Srinidhi CL, Ciga O, Martel AL. Deep neural network models for computational histopathology: A survey. Med Image Anal 2021;67:101813. [PMID: 33049577 DOI: 10.1016/j.media.2020.101813] [Cited by in Crossref: 26] [Cited by in F6Publishing: 24] [Article Influence: 13.0] [Reference Citation Analysis]
30 Djuric U, Zadeh G, Aldape K, Diamandis P. Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care. NPJ Precis Oncol. 2017;1:22. [PMID: 29872706 DOI: 10.1038/s41698-017-0022-1] [Cited by in Crossref: 74] [Cited by in F6Publishing: 65] [Article Influence: 14.8] [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 Jeon T, Kim A, Kim C. Automated immunohistochemical assessment ability to evaluate estrogen and progesterone receptor status compared with quantitative reverse transcription-polymerase chain reaction in breast carcinoma patients. J Pathol Transl Med 2021;55:33-42. [PMID: 33260290 DOI: 10.4132/jptm.2020.09.29] [Reference Citation Analysis]
33 Beinecke J, Heider D. Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making. BioData Min 2021;14:49. [PMID: 34844620 DOI: 10.1186/s13040-021-00283-6] [Reference Citation Analysis]
34 Ortega S, Halicek M, Fabelo H, Camacho R, Plaza ML, Godtliebsen F, M Callicó G, Fei B. Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks. Sensors (Basel) 2020;20:E1911. [PMID: 32235483 DOI: 10.3390/s20071911] [Cited by in Crossref: 10] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
35 Fathi Kazerooni A, Bagley SJ, Akbari H, Saxena S, Bagheri S, Guo J, Chawla S, Nabavizadeh A, Mohan S, Bakas S, Davatzikos C, Nasrallah MP. Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine. Cancers (Basel) 2021;13:5921. [PMID: 34885031 DOI: 10.3390/cancers13235921] [Reference Citation Analysis]
36 Burns JE, Yao J, Summers RM. Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift. J Bone Miner Res 2020;35:28-35. [PMID: 31398274 DOI: 10.1002/jbmr.3849] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
37 Lee H, Han M, Yoo T, Jung C, Son H, Cho M. Evaluation of nuclear chromatin using grayscale intensity and thresholded percentage area in liquid-based cervical cytology. Diagnostic Cytopathology 2018;46:384-9. [DOI: 10.1002/dc.23906] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
38 Coccia M. Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence. Technology in Society 2020;60:101198. [DOI: 10.1016/j.techsoc.2019.101198] [Cited by in Crossref: 38] [Cited by in F6Publishing: 9] [Article Influence: 19.0] [Reference Citation Analysis]
39 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]
40 Lutnick B, Ginley B, Govind D, McGarry SD, LaViolette PS, Yacoub R, Jain S, Tomaszewski JE, Jen KY, Sarder P. An integrated iterative annotation technique for easing neural network training in medical image analysis. Nat Mach Intell 2019;1:112-9. [PMID: 31187088 DOI: 10.1038/s42256-019-0018-3] [Cited by in Crossref: 32] [Cited by in F6Publishing: 29] [Article Influence: 10.7] [Reference Citation Analysis]
41 Abels E, Pantanowitz L, Aeffner F, Zarella MD, van der Laak J, Bui MM, Vemuri VN, Parwani AV, Gibbs J, Agosto-Arroyo E, Beck AH, Kozlowski C. Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association. J Pathol 2019;249:286-94. [PMID: 31355445 DOI: 10.1002/path.5331] [Cited by in Crossref: 68] [Cited by in F6Publishing: 59] [Article Influence: 22.7] [Reference Citation Analysis]
42 Turkki R, Byckhov D, Lundin M, Isola J, Nordling S, Kovanen PE, Verrill C, von Smitten K, Joensuu H, Lundin J, Linder N. Breast cancer outcome prediction with tumour tissue images and machine learning. Breast Cancer Res Treat 2019;177:41-52. [PMID: 31119567 DOI: 10.1007/s10549-019-05281-1] [Cited by in Crossref: 27] [Cited by in F6Publishing: 19] [Article Influence: 9.0] [Reference Citation Analysis]
43 Lee SH, Song IH, Jang HJ. Feasibility of deep learning-based fully automated classification of microsatellite instability in tissue slides of colorectal cancer. Int J Cancer 2021;149:728-40. [PMID: 33851412 DOI: 10.1002/ijc.33599] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
44 Jayasinghe C, Badenhorst P, Jacobs J, Spangenberg G, Smith K. Image‐based high‐throughput phenotyping for the estimation of persistence of perennial ryegrass ( Lolium perenne L.)—A review. Grass Forage Sci 2021;76:321-39. [DOI: 10.1111/gfs.12520] [Reference Citation Analysis]
45 Cong L, Feng W, Yao Z, Zhou X, Xiao W. Deep Learning Model as a New Trend in Computer-aided Diagnosis of Tumor Pathology for Lung Cancer. J Cancer 2020;11:3615-22. [PMID: 32284758 DOI: 10.7150/jca.43268] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
46 Bakas S, Feldman MD. Computational staining of unlabelled tissue. Nat Biomed Eng 2019;3:425-6. [PMID: 31175334 DOI: 10.1038/s41551-019-0414-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
47 Carleton NM, Lee G, Madabhushi A, Veltri RW. Advances in the computational and molecular understanding of the prostate cancer cell nucleus. J Cell Biochem 2018;119:7127-42. [PMID: 29923622 DOI: 10.1002/jcb.27156] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 1.8] [Reference Citation Analysis]
48 Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16:703-715. [PMID: 31399699 DOI: 10.1038/s41571-019-0252-y] [Cited by in Crossref: 191] [Cited by in F6Publishing: 169] [Article Influence: 63.7] [Reference Citation Analysis]
49 Peyster EG, Madabhushi A, Margulies KB. Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection. Transplantation 2018;102:1230-9. [PMID: 29570167 DOI: 10.1097/TP.0000000000002189] [Cited by in Crossref: 8] [Cited by in F6Publishing: 2] [Article Influence: 2.7] [Reference Citation Analysis]
50 Cho KO, Lee SH, Jang HJ. Feasibility of fully automated classification of whole slide images based on deep learning. Korean J Physiol Pharmacol. 2020;24:89-99. [PMID: 31908578 DOI: 10.4196/kjpp.2020.24.1.89] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
51 Kubach J, Muhlebner-Fahrngruber A, Soylemezoglu F, Miyata H, Niehusmann P, Honavar M, Rogerio F, Kim SH, Aronica E, Garbelli R, Vilz S, Popp A, Walcher S, Neuner C, Scholz M, Kuerten S, Schropp V, Roeder S, Eichhorn P, Eckstein M, Brehmer A, Kobow K, Coras R, Blumcke I, Jabari S. Same same but different: A Web-based deep learning application revealed classifying features for the histopathologic distinction of cortical malformations. Epilepsia 2020;61:421-32. [PMID: 32080846 DOI: 10.1111/epi.16447] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
52 Wood-Trageser MA, Lesniak AJ, Demetris AJ. Enhancing the Value of Histopathological Assessment of Allograft Biopsy Monitoring. Transplantation 2019;103:1306-22. [PMID: 30768568 DOI: 10.1097/TP.0000000000002656] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 4.5] [Reference Citation Analysis]
53 Corredor G, Whitney J, Arias V, Madabhushi A, Romero E. Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features. J Med Imaging (Bellingham) 2017;4:021105. [PMID: 28382314 DOI: 10.1117/1.JMI.4.2.021105] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 0.6] [Reference Citation Analysis]
54 Yu KH, Wang F, Berry GJ, Ré C, Altman RB, Snyder M, Kohane IS. Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks. J Am Med Inform Assoc 2020;27:757-69. [PMID: 32364237 DOI: 10.1093/jamia/ocz230] [Cited by in Crossref: 10] [Cited by in F6Publishing: 7] [Article Influence: 10.0] [Reference Citation Analysis]
55 Punchoo R, Bhoora S, Pillay N. Applications of machine learning in the chemical pathology laboratory. J Clin Pathol 2021;74:435-42. [PMID: 34117102 DOI: 10.1136/jclinpath-2021-207393] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
56 Sarwar S, Dent A, Faust K, Richer M, Djuric U, Van Ommeren R, Diamandis P. Physician perspectives on integration of artificial intelligence into diagnostic pathology. NPJ Digit Med 2019;2:28. [PMID: 31304375 DOI: 10.1038/s41746-019-0106-0] [Cited by in Crossref: 37] [Cited by in F6Publishing: 32] [Article Influence: 12.3] [Reference Citation Analysis]
57 Chen S, Jiang L, Zheng X, Shao J, Wang T, Zhang E, Gao F, Wang X, Zheng J. Clinical use of machine learning-based pathomics signature for diagnosis and survival prediction of bladder cancer. Cancer Sci 2021;112:2905-14. [PMID: 33931925 DOI: 10.1111/cas.14927] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
58 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]
59 Xue H, Wang SY, Cui LG, Hong K. Can Contrast-Enhanced Ultrasound Increase or Predict the Success Rate of Testicular Sperm Aspiration in Patients With Azoospermia? AJR Am J Roentgenol 2019;:1-6. [PMID: 30807223 DOI: 10.2214/AJR.18.20436] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
60 Petrick N, Akbar S, Cha KH, Nofech-Mozes S, Sahiner B, Gavrielides MA, Kalpathy-Cramer J, Drukker K, Martel AL; BreastPathQ Challenge Group. SPIE-AAPM-NCI BreastPathQ challenge: an image analysis challenge for quantitative tumor cellularity assessment in breast cancer histology images following neoadjuvant treatment. J Med Imaging (Bellingham) 2021;8:034501. [PMID: 33987451 DOI: 10.1117/1.JMI.8.3.034501] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
61 Hacking SM, Chakraborty B, Nasim R, Vitkovski T, Thomas R. A Holistic Appraisal of Stromal Differentiation in Colorectal Cancer: Biology, Histopathology, Computation, and Genomics. Pathol Res Pract 2021;220:153378. [PMID: 33690050 DOI: 10.1016/j.prp.2021.153378] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
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