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For: 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]
Number Citing Articles
1 Baxi V, Edwards R, Montalto M, Saha S. Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol 2021. [PMID: 34611303 DOI: 10.1038/s41379-021-00919-2] [Reference Citation Analysis]
2 Hassan T, Javed S, Mahmood A, Qaiser T, Werghi N, Rajpoot N. Nucleus Classification in Histology Images Using Message Passing Network. Medical Image Analysis 2022. [DOI: 10.1016/j.media.2022.102480] [Reference Citation Analysis]
3 Ali N, Bolenz C, Todenhöfer T, Stenzel A, Deetmar P, Kriegmair M, Knoll T, Porubsky S, Hartmann A, Popp J, Kriegmair MC, Bocklitz T. Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors. Sci Rep 2021;11:11629. [PMID: 34079004 DOI: 10.1038/s41598-021-91081-x] [Reference Citation Analysis]
4 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]
5 Puladi B, Ooms M, Kintsler S, Houschyar KS, Steib F, Modabber A, Hölzle F, Knüchel-Clarke R, Braunschweig T. Automated PD-L1 Scoring Using Artificial Intelligence in Head and Neck Squamous Cell Carcinoma. Cancers (Basel) 2021;13:4409. [PMID: 34503218 DOI: 10.3390/cancers13174409] [Reference Citation Analysis]
6 Xue Z, Zhang T, Lin L. Progress prediction of Parkinson's disease based on graph wavelet transform and attention weighted random forest. Expert Systems with Applications 2022;203:117483. [DOI: 10.1016/j.eswa.2022.117483] [Reference Citation Analysis]
7 Madabhushi A, Reyes-Aldasoro CC. Special issue on computational pathology: An overview. Med Image Anal 2021;73:102151. [PMID: 34329904 DOI: 10.1016/j.media.2021.102151] [Reference Citation Analysis]
8 Mehrvar S, Himmel LE, Babburi P, Goldberg AL, Guffroy M, Janardhan K, Krempley AL, Bawa B. Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives. J Pathol Inform 2021;12:42. [PMID: 34881097 DOI: 10.4103/jpi.jpi_36_21] [Reference Citation Analysis]
9 Rashmi R, Prasad K, Udupa CBK. Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review. J Med Syst 2021;46:7. [PMID: 34860316 DOI: 10.1007/s10916-021-01786-9] [Reference Citation Analysis]
10 Roth A, Wüstefeld K, Weichert F. A Data-Centric Augmentation Approach for Disturbed Sensor Image Segmentation. J Imaging 2021;7:206. [PMID: 34677292 DOI: 10.3390/jimaging7100206] [Reference Citation Analysis]
11 Konstantinov AV, Utkin LV. Multi-attention multiple instance learning. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07259-5] [Reference Citation Analysis]
12 Pal A, Xue Z, Desai K, Aina F Banjo A, Adepiti CA, Long LR, Schiffman M, Antani S. Deep multiple-instance learning for abnormal cell detection in cervical histopathology images. Comput Biol Med 2021;138:104890. [PMID: 34601391 DOI: 10.1016/j.compbiomed.2021.104890] [Reference Citation Analysis]
13 Xu Y, Jiang L, Huang S, Liu Z, Zhang J. Dual resolution deep learning network with self-attention mechanism for classification and localisation of colorectal cancer in histopathological images. J Clin Pathol 2022:jclinpath-2021-208042. [PMID: 35273120 DOI: 10.1136/jclinpath-2021-208042] [Reference Citation Analysis]
14 Kather JN, Heij LR, Grabsch HI, Loeffler C, Echle A, Muti HS, Krause J, Niehues JM, Sommer KAJ, Bankhead P, Kooreman LFS, Schulte JJ, Cipriani NA, Buelow RD, Boor P, Ortiz-Brüchle NN, Hanby AM, Speirs V, Kochanny S, Patnaik A, Srisuwananukorn A, Brenner H, Hoffmeister M, van den Brandt PA, Jäger D, Trautwein C, Pearson AT, Luedde T. Pan-cancer image-based detection of clinically actionable genetic alterations. Nat Cancer 2020;1:789-99. [PMID: 33763651 DOI: 10.1038/s43018-020-0087-6] [Cited by in Crossref: 52] [Cited by in F6Publishing: 42] [Article Influence: 26.0] [Reference Citation Analysis]
15 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]
16 Liu H, Xu W, Shang Z, Wang X, Zhou H, Ma K, Zhou H, Qi J, Jiang J, Tan L, Zeng H, Cai H, Wang K, Qian Y. Breast Cancer Molecular Subtype Prediction on Pathological Images with Discriminative Patch Selection and Multi-Instance Learning. Front Oncol 2022;12:858453. [DOI: 10.3389/fonc.2022.858453] [Reference Citation Analysis]
17 Srinidhi CL, Kim SW, Chen FD, Martel AL. Self-supervised driven consistency training for annotation efficient histopathology image analysis. Med Image Anal 2021;75:102256. [PMID: 34717189 DOI: 10.1016/j.media.2021.102256] [Reference Citation Analysis]
18 Klimov S, Xue Y, Gertych A, Graham RP, Jiang Y, Bhattarai S, Pandol SJ, Rakha EA, Reid MD, Aneja R. Predicting Metastasis Risk in Pancreatic Neuroendocrine Tumors Using Deep Learning Image Analysis. Front Oncol 2020;10:593211. [PMID: 33718106 DOI: 10.3389/fonc.2020.593211] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
19 Zhang X, Zhu X, Tang K, Zhao Y, Lu Z, Feng Q. DDTNet: A dense dual-task network for tumor-infiltrating lymphocyte detection and segmentation in histopathological images of breast cancer. Med Image Anal 2022;78:102415. [PMID: 35339950 DOI: 10.1016/j.media.2022.102415] [Reference Citation Analysis]
20 Esteva A, Chou K, Yeung S, Naik N, Madani A, Mottaghi A, Liu Y, Topol E, Dean J, Socher R. Deep learning-enabled medical computer vision. NPJ Digit Med. 2021;4:5. [PMID: 33420381 DOI: 10.1038/s41746-020-00376-2] [Cited by in Crossref: 16] [Cited by in F6Publishing: 9] [Article Influence: 16.0] [Reference Citation Analysis]
21 Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 2021;8:53. [PMID: 33816053 DOI: 10.1186/s40537-021-00444-8] [Cited by in Crossref: 29] [Cited by in F6Publishing: 12] [Article Influence: 29.0] [Reference Citation Analysis]
22 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]
23 Yang H, Chen L, Cheng Z, Yang M, Wang J, Lin C, Wang Y, Huang L, Chen Y, Peng S, Ke Z, Li W. Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study. BMC Med 2021;19:80. [PMID: 33775248 DOI: 10.1186/s12916-021-01953-2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 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]
25 Pérez-Bueno F, Vega M, Sales MA, Aneiros-Fernández J, Naranjo V, Molina R, Katsaggelos AK. Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference. Comput Methods Programs Biomed 2021;211:106453. [PMID: 34649072 DOI: 10.1016/j.cmpb.2021.106453] [Reference Citation Analysis]
26 Zheng L, Wang H, Mei L, Chen Q, Zhang Y, Zhang H. Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks. Ann Transl Med 2021;9:763. [PMID: 34268376 DOI: 10.21037/atm-21-119] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
27 van der Kamp A, Waterlander TJ, de Bel T, van der Laak J, van den Heuvel-Eibrink MM, Mavinkurve-Groothuis AMC, de Krijger RR. Artificial Intelligence in Pediatric Pathology: The Extinction of a Medical Profession or the Key to a Bright Future? Pediatr Dev Pathol 2022;:10935266211059809. [PMID: 35238696 DOI: 10.1177/10935266211059809] [Reference Citation Analysis]
28 Muñoz-Aguirre M, Ntasis VF, Rojas S, Guigó R. PyHIST: A Histological Image Segmentation Tool. PLoS Comput Biol 2020;16:e1008349. [PMID: 33075075 DOI: 10.1371/journal.pcbi.1008349] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
29 Liotta LA, Pappalardo PA, Carpino A, Haymond A, Howard M, Espina V, Wulfkuhle J, Petricoin E. Laser Capture Proteomics: Spatial Tissue Molecular Profiling from the bench to personalized medicine. Expert Rev Proteomics 2021. [PMID: 34607525 DOI: 10.1080/14789450.2021.1984886] [Reference Citation Analysis]
30 Garland J, Hu M, Duffy M, Kesha K, Glenn C, Morrow P, Stables S, Ondruschka B, Da Broi U, Tse RD. Classifying Microscopic Acute and Old Myocardial Infarction Using Convolutional Neural Networks. Am J Forensic Med Pathol 2021;42:230-4. [PMID: 33833193 DOI: 10.1097/PAF.0000000000000672] [Reference Citation Analysis]
31 Mathew T, Ajith B, Kini JR, Rajan J. Deep learning‐based automated mitosis detection in histopathology images for breast cancer grading. Int J Imaging Syst Tech. [DOI: 10.1002/ima.22703] [Reference Citation Analysis]
32 Zhuang H, Zhang J, Liao F. A systematic review on application of deep learning in digestive system image processing. Vis Comput 2021;:1-16. [PMID: 34744231 DOI: 10.1007/s00371-021-02322-z] [Reference Citation Analysis]
33 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]
34 Geread RS, Sivanandarajah A, Brouwer ER, Wood GA, Androutsos D, Faragalla H, Khademi A. piNET-An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images. Cancers (Basel) 2020;13:E11. [PMID: 33375043 DOI: 10.3390/cancers13010011] [Reference Citation Analysis]
35 Zhang C, Jiang H, Jiang H, Xi H, Chen B, Liu Y, Juhas M, Li J, Zhang Y. Deep Learning for Microscopic Examination of Protozoan Parasites. Computational and Structural Biotechnology Journal 2022. [DOI: 10.1016/j.csbj.2022.02.005] [Reference Citation Analysis]
36 Xu Z, Verma A, Naveed U, Bakhoum SF, Khosravi P, Elemento O. Deep learning predicts chromosomal instability from histopathology images. iScience 2021;24:102394. [PMID: 33997679 DOI: 10.1016/j.isci.2021.102394] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
37 Sabbih GO, Danquah MK. Neuroblastoma GD2 Expression and Computational Analysis of Aptamer-Based Bioaffinity Targeting. Int J Mol Sci 2021;22:9101. [PMID: 34445807 DOI: 10.3390/ijms22169101] [Reference Citation Analysis]
38 Pan S, Fu Y, Chen P, Liu J, Liu W, Wang X, Cai G, Yin Z, Wu J, Tang L, Wang Y, Duan S, Dai N, Jiang L, Xu M, Chen X. Multi-Task Learning-Based Immunofluorescence Classification of Kidney Disease. Int J Environ Res Public Health 2021;18:10798. [PMID: 34682567 DOI: 10.3390/ijerph182010798] [Reference Citation Analysis]
39 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]
40 Diao JA, Wang JK, Chui WF, Mountain V, Gullapally SC, Srinivasan R, Mitchell RN, Glass B, Hoffman S, Rao SK, Maheshwari C, Lahiri A, Prakash A, McLoughlin R, Kerner JK, Resnick MB, Montalto MC, Khosla A, Wapinski IN, Beck AH, Elliott HL, Taylor-Weiner A. Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes. Nat Commun 2021;12:1613. [PMID: 33712588 DOI: 10.1038/s41467-021-21896-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 9] [Article Influence: 3.0] [Reference Citation Analysis]
41 van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat Med 2021;27:775-84. [PMID: 33990804 DOI: 10.1038/s41591-021-01343-4] [Cited by in Crossref: 4] [Cited by in F6Publishing: 10] [Article Influence: 4.0] [Reference Citation Analysis]
42 Pettersen HS, Belevich I, Røyset ES, Smistad E, Simpson MR, Jokitalo E, Reinertsen I, Bakke I, Pedersen A. Code-Free Development and Deployment of Deep Segmentation Models for Digital Pathology. Front Med 2022;8:816281. [DOI: 10.3389/fmed.2021.816281] [Reference Citation Analysis]
43 Budelmann D, Laue H, Weiss N, Dahmen U, D'Alessandro LA, Biermayer I, Klingmüller U, Ghallab A, Hassan R, Begher-Tibbe B, Hengstler JG, Schwen LO. Automated Detection of Portal Fields and Central Veins in Whole-Slide Images of Liver Tissue. J Pathol Inform 2022;13:100001. [PMID: 35242441 DOI: 10.1016/j.jpi.2022.100001] [Reference Citation Analysis]
44 Raulf AP, Butke J, Menzen L, Küpper C, Großerueschkamp F, Gerwert K, Mosig A. A representation learning approach for recovering scatter-corrected spectra from Fourier-transform infrared spectra of tissue samples. J Biophotonics 2021;14:e202000385. [PMID: 33295130 DOI: 10.1002/jbio.202000385] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
45 Gupta P, Huang Y, Sahoo PK, You JF, Chiang SF, Onthoni DD, Chern YJ, Chao KY, Chiang JM, Yeh CY, Tsai WS. Colon Tissues Classification and Localization in Whole Slide Images Using Deep Learning. Diagnostics (Basel) 2021;11:1398. [PMID: 34441332 DOI: 10.3390/diagnostics11081398] [Reference Citation Analysis]
46 Tan K, Huang W, Liu X, Hu J, Dong S. A multi-modal fusion framework based on multi-task correlation learning for cancer prognosis prediction. Artificial Intelligence in Medicine 2022;126:102260. [DOI: 10.1016/j.artmed.2022.102260] [Reference Citation Analysis]