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Items: 1 to 20 of 57

1.

Deep Fusion of Contextual and Object-based Representations for Delineation of Multiple Nuclear Phenotypes.

Khoshdeli M, Winkelmaier G, Parvin B.

Bioinformatics. 2019 May 28. pii: btz430. doi: 10.1093/bioinformatics/btz430. [Epub ahead of print]

PMID:
31135022
2.

Deep Learning Models Differentiate Tumor Grades from H&E Stained Histology Sections.

Khoshdeli M, Borowsky A, Parvin B.

Conf Proc IEEE Eng Med Biol Soc. 2018 Jul;2018:620-623. doi: 10.1109/EMBC.2018.8512357.

PMID:
30440473
3.

Fusion of encoder-decoder deep networks improves delineation of multiple nuclear phenotypes.

Khoshdeli M, Winkelmaier G, Parvin B.

BMC Bioinformatics. 2018 Aug 7;19(1):294. doi: 10.1186/s12859-018-2285-0.

4.

High-Dimensional Phenotyping Identifies Age-Emergent Cells in Human Mammary Epithelia.

Pelissier Vatter FA, Schapiro D, Chang H, Borowsky AD, Lee JK, Parvin B, Stampfer MR, LaBarge MA, Bodenmiller B, Lorens JB.

Cell Rep. 2018 Apr 24;23(4):1205-1219. doi: 10.1016/j.celrep.2018.03.114.

5.

Feature-Based Representation Improves Color Decomposition and Nuclear Detection Using a Convolutional Neural Network.

Khoshdeli M, Parvin B.

IEEE Trans Biomed Eng. 2018 Mar;65(3):625-634. doi: 10.1109/TBME.2017.2711529.

6.

Buckyballs conjugated with nucleic acid sequences identifies microorganisms in live cell assays.

Cheng Q, Parvin B.

J Nanobiotechnology. 2017 Nov 9;15(1):78. doi: 10.1186/s12951-017-0315-0.

7.

Detection of Nuclei in H&E Stained Sections Using Convolutional Neural Networks.

Khoshdeli M, Cong R, Parvin B.

IEEE EMBS Int Conf Biomed Health Inform. 2017 Feb;2017:105-108. doi: 10.1109/BHI.2017.7897216. Epub 2017 Apr 13.

8.

Integrative Analysis of Cellular Morphometric Context Reveals Clinically Relevant Signatures in Lower Grade Glioma.

Han J, Wang Y, Cai W, Borowsky A, Parvin B, Chang H.

Med Image Comput Comput Assist Interv. 2016 Oct;9900:72-80. doi: 10.1007/978-3-319-46720-7_9. Epub 2016 Oct 2.

9.

When machine vision meets histology: A comparative evaluation of model architecture for classification of histology sections.

Zhong C, Han J, Borowsky A, Parvin B, Wang Y, Chang H.

Med Image Anal. 2017 Jan;35:530-543. doi: 10.1016/j.media.2016.08.010. Epub 2016 Sep 9.

10.

Erratum: Stiffness of the microenvironment upregulates ERBB2 expression in 3D cultures of MCF10A within the range of mammographic density.

Cheng Q, Bilgin CC, Fontenay G, Chang H, Henderson M, Han J, Parvin B.

Sci Rep. 2016 Aug 24;6:31680. doi: 10.1038/srep31680. No abstract available.

11.

Stiffness of the microenvironment upregulates ERBB2 expression in 3D cultures of MCF10A within the range of mammographic density.

Cheng Q, Bilgin CC, Fontenay G, Chang H, Henderson M, Han J, Parvin B.

Sci Rep. 2016 Jul 7;6:28987. doi: 10.1038/srep28987. Erratum in: Sci Rep. 2016 Aug 24;6:31680.

12.

BioSig3D: High Content Screening of Three-Dimensional Cell Culture Models.

Bilgin CC, Fontenay G, Cheng Q, Chang H, Han J, Parvin B.

PLoS One. 2016 Mar 15;11(3):e0148379. doi: 10.1371/journal.pone.0148379. eCollection 2016.

13.

Machine Learning and Data Mining in Medical Imaging.

Shen D, Zhang D, Young A, Parvin B.

IEEE J Biomed Health Inform. 2015 Sep;19(5):1587-8. No abstract available.

PMID:
26574616
14.

Functionalized Buckyballs for Visualizing Microbial Species in Different States and Environments.

Cheng Q, Aravind A, Buckley M, Gifford A, Parvin B.

Sci Rep. 2015 Sep 8;5:13685. doi: 10.1038/srep13685.

15.

Stacked Predictive Sparse Decomposition for Classification of Histology Sections.

Chang H, Zhou Y, Borowsky A, Barner K, Spellman P, Parvin B.

Int J Comput Vis. 2015 May;113(1):3-18. Epub 2014 Dec 23.

16.

NUCLEI SEGMENTATION VIA SPARSITY CONSTRAINED CONVOLUTIONAL REGRESSION.

Zhou Y, Chang H, Barner KE, Parvin B.

Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:1284-1287. doi: 10.1109/ISBI.2015.7164109. Epub 2015 Jul 23.

17.
18.

Scaling up graph-based semisupervised learning via prototype vector machines.

Zhang K, Lan L, Kwok JT, Vucetic S, Parvin B.

IEEE Trans Neural Netw Learn Syst. 2015 Mar;26(3):444-57. doi: 10.1109/TNNLS.2014.2315526.

19.

Classification of Histology Sections via Multispectral Convolutional Sparse Coding.

Zhou Y, Chang H, Barner K, Spellman P, Parvin B.

Conf Comput Vis Pattern Recognit Workshops. 2014 Jun;2014:3081-3088.

20.

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