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For: Salvi M, Acharya UR, Molinari F, Meiburger KM. The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis. Computers in Biology and Medicine 2021;128:104129. [DOI: 10.1016/j.compbiomed.2020.104129] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 11.0] [Reference Citation Analysis]
Number Citing Articles
1 Zhang J, Wang X, Ni G, Liu J, Hao R, Liu L, Liu Y, Du X, Xu F. Fast and accurate automated recognition of the dominant cells from fecal images based on Faster R-CNN. Sci Rep 2021;11:10361. [PMID: 33990662 DOI: 10.1038/s41598-021-89863-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Chen H, Strickland AL, Castrillon DH. Histopathologic diagnosis of endometrial precancers: Updates and future directions. Seminars in Diagnostic Pathology 2021. [DOI: 10.1053/j.semdp.2021.12.001] [Reference Citation Analysis]
3 Jang HJ, Lee A, Kang J, Song IH, Lee SH. Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach. World J Gastroenterol 2021; 27(44): 7687-7704 [PMID: 34908807 DOI: 10.3748/wjg.v27.i44.7687] [Reference Citation Analysis]
4 You Z, Jiang M, Shi Z, Ning X, Shi C, Du S, Hérard AS, Jan C, Souedet N, Delzescaux T. Evaluation of automated segmentation algorithms for neurons in macaque cerebral microscopic images. Microsc Res Tech 2021. [PMID: 33908123 DOI: 10.1002/jemt.23786] [Reference Citation Analysis]
5 Jiao Y, Li J, Qian C, Fei S. Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images. Comput Methods Programs Biomed 2021;204:106047. [PMID: 33789213 DOI: 10.1016/j.cmpb.2021.106047] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
6 Bussola N, Papa B, Melaiu O, Castellano A, Fruci D, Jurman G. Quantification of the Immune Content in Neuroblastoma: Deep Learning and Topological Data Analysis in Digital Pathology. Int J Mol Sci 2021;22:8804. [PMID: 34445517 DOI: 10.3390/ijms22168804] [Reference Citation Analysis]
7 Salvi M, Molinari F, Iussich S, Muscatello LV, Pazzini L, Benali S, Banco B, Abramo F, De Maria R, Aresu L. Histopathological Classification of Canine Cutaneous Round Cell Tumors Using Deep Learning: A Multi-Center Study. Front Vet Sci 2021;8:640944. [PMID: 33869320 DOI: 10.3389/fvets.2021.640944] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
8 Qu H, Minacapelli CD, Tait C, Gupta K, Bhurwal A, Catalano C, Dafalla R, Metaxas D, Rustgi VK. Training of computational algorithms to predict NAFLD activity score and fibrosis stage from liver histopathology slides. Comput Methods Programs Biomed 2021;207:106153. [PMID: 34020377 DOI: 10.1016/j.cmpb.2021.106153] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
9 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]
10 Jiao Y, Yuan J, Qiang Y, Fei S. Deep embeddings and logistic regression for rapid active learning in histopathological images. Comput Methods Programs Biomed 2021;212:106464. [PMID: 34736166 DOI: 10.1016/j.cmpb.2021.106464] [Reference Citation Analysis]
11 Zhang L, Li M, Wu Y, Hao F, Wang C, Han W, Niu D, Zheng W. Classification of renal biopsy direct immunofluorescence image using multiple attention convolutional neural network. Comput Methods Programs Biomed 2021;:106532. [PMID: 34852936 DOI: 10.1016/j.cmpb.2021.106532] [Reference Citation Analysis]
12 Amin J, Sharif M, Fernandes SL, Wang SH, Saba T, Khan AR. Breast microscopic cancer segmentation and classification using unique 4-qubit-quantum model. Microsc Res Tech 2022. [PMID: 35043505 DOI: 10.1002/jemt.24054] [Reference Citation Analysis]
13 Koh JEW, De Michele S, Sudarshan VK, Jahmunah V, Ciaccio EJ, Ooi CP, Gururajan R, Gururajan R, Oh SL, Lewis SK, Green PH, Bhagat G, Acharya UR. Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach. Comput Methods Programs Biomed 2021;203:106010. [PMID: 33831693 DOI: 10.1016/j.cmpb.2021.106010] [Reference Citation Analysis]
14 Zhang J, Li C, Rahaman MM, Yao Y, Ma P, Zhang J, Zhao X, Jiang T, Grzegorzek M. A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches. Artif Intell Rev 2021;:1-70. [PMID: 34602697 DOI: 10.1007/s10462-021-10082-4] [Reference Citation Analysis]