Format
Sort by
Items per page

Send to

Choose Destination

Search results

Items: 1 to 20 of 94

1.

Discriminative Scale Learning (DiScrn): Applications to Prostate Cancer Detection from MRI and Needle Biopsies.

Wang H, Viswanath S, Madabhushi A.

Sci Rep. 2017 Sep 28;7(1):12375. doi: 10.1038/s41598-017-12569-z.

2.

Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation.

Li L, Pahwa S, Penzias G, Rusu M, Gollamudi J, Viswanath S, Madabhushi A.

Sci Rep. 2017 Aug 18;7(1):8717. doi: 10.1038/s41598-017-08969-w.

3.

Cascaded Multi-view Canonical Correlation (CaMCCo) for Early Diagnosis of Alzheimer's Disease via Fusion of Clinical, Imaging and Omic Features.

Singanamalli A, Wang H, Madabhushi A; Alzheimer’s Disease Neuroimaging Initiative.

Sci Rep. 2017 Aug 15;7(1):8137. doi: 10.1038/s41598-017-03925-0.

4.

Single cell qPCR reveals that additional HAND2 and microRNA-1 facilitate the early reprogramming progress of seven-factor-induced human myocytes.

Bektik E, Dennis A, Prasanna P, Madabhushi A, Fu JD.

PLoS One. 2017 Aug 10;12(8):e0183000. doi: 10.1371/journal.pone.0183000. eCollection 2017.

5.

Erratum to: Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.

Braman NM, Etesami M, Prasanna P, Dubchuk C, Gilmore H, Tiwari P, Plecha D, Madabhushi A.

Breast Cancer Res. 2017 Jul 10;19(1):80. doi: 10.1186/s13058-017-0862-1. No abstract available.

6.

Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.

Braman NM, Etesami M, Prasanna P, Dubchuk C, Gilmore H, Tiwari P, Plecha D, Madabhushi A.

Breast Cancer Res. 2017 May 18;19(1):57. doi: 10.1186/s13058-017-0846-1. Erratum in: Breast Cancer Res. 2017 Jul 10;19(1):80.

7.

Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent.

Cruz-Roa A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih NNC, Tomaszewski J, González FA, Madabhushi A.

Sci Rep. 2017 Apr 18;7:46450. doi: 10.1038/srep46450.

8.

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images.

Xu J, Luo X, Wang G, Gilmore H, Madabhushi A.

Neurocomputing. 2016 May 26;191:214-223. doi: 10.1016/j.neucom.2016.01.034. Epub 2016 Feb 17.

9.

Computational imaging reveals shape differences between normal and malignant prostates on MRI.

Rusu M, Purysko AS, Verma S, Kiechle J, Gollamudi J, Ghose S, Herrmann K, Gulani V, Paspulati R, Ponsky L, Böhm M, Haynes AM, Moses D, Shnier R, Delprado W, Thompson J, Stricker P, Madabhushi A.

Sci Rep. 2017 Feb 1;7:41261. doi: 10.1038/srep41261.

10.

Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases.

Viswanath SE, Tiwari P, Lee G, Madabhushi A; Alzheimer’s Disease Neuroimaging Initiative.

BMC Med Imaging. 2017 Jan 5;17(1):2. doi: 10.1186/s12880-016-0172-6.

11.

Optical High Content Nanoscopy of Epigenetic Marks Decodes Phenotypic Divergence in Stem Cells.

Kim JJ, Bennett NK, Devita MS, Chahar S, Viswanath S, Lee EA, Jung G, Shao PP, Childers EP, Liu S, Kulesa A, Garcia BA, Becker ML, Hwang NS, Madabhushi A, Verzi MP, Moghe PV.

Sci Rep. 2017 Jan 4;7:39406. doi: 10.1038/srep39406.

12.

Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor.

Prasanna P, Tiwari P, Madabhushi A.

Sci Rep. 2016 Nov 22;6:37241. doi: 10.1038/srep37241.

13.

Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI.

Shiradkar R, Podder TK, Algohary A, Viswanath S, Ellis RJ, Madabhushi A.

Radiat Oncol. 2016 Nov 10;11(1):148.

14.

Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study.

Tiwari P, Prasanna P, Wolansky L, Pinho M, Cohen M, Nayate AP, Gupta A, Singh G, Hatanpaa KJ, Sloan A, Rogers L, Madabhushi A.

AJNR Am J Neuroradiol. 2016 Dec;37(12):2231-2236. Epub 2016 Sep 15.

15.

Multi-Pass Adaptive Voting for Nuclei Detection in Histopathological Images.

Lu C, Xu H, Xu J, Gilmore H, Mandal M, Madabhushi A.

Sci Rep. 2016 Oct 3;6:33985. doi: 10.1038/srep33985.

16.

Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images.

Romo-Bucheli D, Janowczyk A, Gilmore H, Romero E, Madabhushi A.

Sci Rep. 2016 Sep 7;6:32706. doi: 10.1038/srep32706.

17.

Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.

Janowczyk A, Madabhushi A.

J Pathol Inform. 2016 Jul 26;7:29. doi: 10.4103/2153-3539.186902. eCollection 2016.

18.

AutoStitcher: An Automated Program for Efficient and Robust Reconstruction of Digitized Whole Histological Sections from Tissue Fragments.

Penzias G, Janowczyk A, Singanamalli A, Rusu M, Shih N, Feldman M, Stricker PD, Delprado W, Tiwari S, Böhm M, Haynes AM, Ponsky L, Viswanath S, Madabhushi A.

Sci Rep. 2016 Jul 26;6:29906. doi: 10.1038/srep29906.

19.

Image analysis and machine learning in digital pathology: Challenges and opportunities.

Madabhushi A, Lee G.

Med Image Anal. 2016 Oct;33:170-175. doi: 10.1016/j.media.2016.06.037. Epub 2016 Jul 4.

20.

Adaptive Dimensionality Reduction with Semi-Supervision (AdDReSS): Classifying Multi-Attribute Biomedical Data.

Lee G, Romo Bucheli DE, Madabhushi A.

PLoS One. 2016 Jul 15;11(7):e0159088. doi: 10.1371/journal.pone.0159088. eCollection 2016.

Supplemental Content

Loading ...
Support Center