Madabhushi A - Search Results

1

A machine learning model for separating epithelial and stromal regions in oral cavity squamous cell carcinomas using H&E-stained histology images: A multi-center, retrospective study.

Wu Y, Koyuncu CF, Toro P, Corredor G, Feng Q, Buzzy C, Old M, Teknos T, Connelly ST, Jordan RC, Lang Kuhs KA, Lu C, Lewis JS Jr, Madabhushi A. Wu Y, et al. Among authors: madabhushi a. Oral Oncol. 2022 Aug;131:105942. doi: 10.1016/j.oraloncology.2022.105942. Epub 2022 Jun 8. Oral Oncol. 2022. PMID: 35689952

2

A quantitative histomorphometric classifier (QuHbIC) identifies aggressive versus indolent p16-positive oropharyngeal squamous cell carcinoma.

Lewis JS Jr, Ali S, Luo J, Thorstad WL, Madabhushi A. Lewis JS Jr, et al. Among authors: madabhushi a. Am J Surg Pathol. 2014 Jan;38(1):128-37. doi: 10.1097/PAS.0000000000000086. Am J Surg Pathol. 2014. PMID: 24145650 Free PMC article.

3

Spatially aware cell cluster(spACC1) graphs: predicting outcome in oropharyngeal pl6+ tumors.

Ali S, Lewis J, Madabhushi A. Ali S, et al. Among authors: madabhushi a. Med Image Comput Comput Assist Interv. 2013;16(Pt 1):412-9. doi: 10.1007/978-3-642-40811-3_52. Med Image Comput Comput Assist Interv. 2013. PMID: 24505693

4

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

Madabhushi A, Lee G. Madabhushi A, et al. Med Image Anal. 2016 Oct;33:170-175. doi: 10.1016/j.media.2016.06.037. Epub 2016 Jul 4. Med Image Anal. 2016. PMID: 27423409 Free PMC article. Review.

5

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

Lu C, Xu H, Xu J, Gilmore H, Mandal M, Madabhushi A. Lu C, et al. Among authors: madabhushi a. Sci Rep. 2016 Oct 3;6:33985. doi: 10.1038/srep33985. Sci Rep. 2016. PMID: 27694950 Free PMC article.

6

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. Shiradkar R, et al. Among authors: madabhushi a. Radiat Oncol. 2016 Nov 10;11(1):148. doi: 10.1186/s13014-016-0718-3. Radiat Oncol. 2016. PMID: 27829431 Free PMC article.

7

Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features.

Corredor G, Whitney J, Arias V, Madabhushi A, Romero E. Corredor G, et al. Among authors: madabhushi a. J Med Imaging (Bellingham). 2017 Apr;4(2):021105. doi: 10.1117/1.JMI.4.2.021105. Epub 2017 Mar 11. J Med Imaging (Bellingham). 2017. PMID: 28382314 Free PMC article.

8

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. Cruz-Roa A, et al. Among authors: madabhushi a. Sci Rep. 2017 Apr 18;7:46450. doi: 10.1038/srep46450. Sci Rep. 2017. PMID: 28418027 Free PMC article.

9

An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival.

Lu C, Lewis JS Jr, Dupont WD, Plummer WD Jr, Janowczyk A, Madabhushi A. Lu C, et al. Among authors: madabhushi a. Mod Pathol. 2017 Dec;30(12):1655-1665. doi: 10.1038/modpathol.2017.98. Epub 2017 Aug 4. Mod Pathol. 2017. PMID: 28776575 Free PMC article.

10

A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue.

Nirschl JJ, Janowczyk A, Peyster EG, Frank R, Margulies KB, Feldman MD, Madabhushi A. Nirschl JJ, et al. Among authors: madabhushi a. PLoS One. 2018 Apr 3;13(4):e0192726. doi: 10.1371/journal.pone.0192726. eCollection 2018. PLoS One. 2018. PMID: 29614076 Free PMC article.

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