| Year |
Citation |
Score |
| 2023 |
Koyuncu C, Janowczyk A, Farre X, Pathak T, Mirtti T, Fernandez PL, Pons L, Reder NP, Serafin R, Chow SS, Viswanathan VS, Glaser AK, True LD, Liu JT, Madabhushi A. Visual assessment of 2D levels within 3D pathology datasets of prostate needle biopsies reveals substantial spatial heterogeneity. Laboratory Investigation; a Journal of Technical Methods and Pathology. 100265. PMID 37858679 DOI: 10.1016/j.labinv.2023.100265 |
0.476 |
|
| 2023 |
Serafin R, Koyuncu C, Xie W, Huang H, Glaser AK, Reder NP, Janowczyk A, True LD, Madabhushi A, Liu JT. Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment. The Journal of Pathology. PMID 37232213 DOI: 10.1002/path.6090 |
0.494 |
|
| 2023 |
Chen Y, Li H, Janowczyk A, Toro P, Corredor G, Whitney J, Lu C, Koyuncu CF, Mokhtari M, Buzzy C, Ganesan S, Feldman MD, Fu P, Corbin H, Harbhajanka A, et al. Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer. Npj Breast Cancer. 9: 40. PMID 37198173 DOI: 10.1038/s41523-023-00545-y |
0.48 |
|
| 2023 |
Chen Y, Zee J, Janowczyk AR, Rubin J, Toro P, Lafata KJ, Mariani LH, Holzman LB, Hodgin JB, Madabhushi A, Barisoni L. Clinical Relevance of Computationally Derived Attributes of Peritubular Capillaries from Kidney Biopsies. Kidney360. PMID 37016482 DOI: 10.34067/KID.0000000000000116 |
0.444 |
|
| 2022 |
Zhou Y, Koyuncu C, Lu C, Grobholz R, Katz I, Madabhushi A, Janowczyk A. Multi-site cross-organ calibrated deep learning (MuSClD): Automated diagnosis of non-melanoma skin cancer. Medical Image Analysis. 84: 102702. PMID 36516556 DOI: 10.1016/j.media.2022.102702 |
0.455 |
|
| 2022 |
Arabyarmohammadi S, Leo P, Viswanathan VS, Janowczyk A, Corredor G, Fu P, Meyerson H, Metheny L, Madabhushi A. Machine Learning to Predict Risk of Relapse Using Cytologic Image Markers in Patients With Acute Myeloid Leukemia Posthematopoietic Cell Transplantation. Jco Clinical Cancer Informatics. 6: e2100156. PMID 35522898 DOI: 10.1200/CCI.21.00156 |
0.457 |
|
| 2021 |
Xie W, Reder NP, Koyuncu CF, Leo P, Hawley S, Huang H, Mao C, Postupna N, Kang S, Serafin R, Gao G, Han Q, Bishop KW, Barner LA, Fu P, ... ... Janowczyk A, et al. Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis. Cancer Research. PMID 34853071 DOI: 10.1158/0008-5472.CAN-21-2843 |
0.508 |
|
| 2021 |
Miao R, Toth R, Zhou Y, Madabhushi A, Janowczyk A. Quick Annotator: an open-source digital pathology based rapid image annotation tool. The Journal of Pathology. Clinical Research. PMID 34288586 DOI: 10.1002/cjp2.229 |
0.487 |
|
| 2021 |
Peyster EG, Arabyarmohammadi S, Janowczyk A, Azarianpour-Esfahani S, Sekulic M, Cassol C, Blower L, Parwani A, Lal P, Feldman MD, Margulies KB, Madabhushi A. An automated computational image analysis pipeline for histological grading of cardiac allograft rejection. European Heart Journal. PMID 33982079 DOI: 10.1093/eurheartj/ehab241 |
0.448 |
|
| 2021 |
Leo P, Janowczyk A, Elliott R, Janaki N, Bera K, Shiradkar R, Farré X, Fu P, El-Fahmawi A, Shahait M, Kim J, Lee D, Yamoah K, Rebbeck TR, Khani F, et al. Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study. Npj Precision Oncology. 5: 35. PMID 33941830 DOI: 10.1038/s41698-021-00174-3 |
0.528 |
|
| 2021 |
Leo P, Chandramouli S, Farré X, Elliott R, Janowczyk A, Bera K, Fu P, Janaki N, El-Fahmawi A, Shahait M, Kim J, Lee D, Yamoah K, Rebbeck TR, Khani F, et al. Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2. European Urology Focus. PMID 33941504 DOI: 10.1016/j.euf.2021.04.016 |
0.47 |
|
| 2020 |
Lu C, Koyuncu C, Corredor G, Prasanna P, Leo P, Wang X, Janowczyk A, Bera K, Lewis J, Velcheti V, Madabhushi A. Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers. Medical Image Analysis. 68: 101903. PMID 33352373 DOI: 10.1016/j.media.2020.101903 |
0.485 |
|
| 2020 |
Chen Y, Zee J, Smith A, Jayapandian C, Hodgin J, Howell D, Palmer M, Thomas D, Cassol C, Farris AB, Perkinson K, Madabhushi A, Barisoni L, Janowczyk A. Assessment of a computerized quantitative quality control tool for kidney whole slide image biopsies. The Journal of Pathology. PMID 33197281 DOI: 10.1002/path.5590 |
0.535 |
|
| 2020 |
Sadri AR, Janowczyk A, Zhou R, Verma R, Beig N, Antunes J, Madabhushi A, Tiwari P, Viswanath SE. Technical Note: MRQy - An Open-Source Tool for Quality Control of MR Imaging Data. Medical Physics. PMID 33176026 DOI: 10.1002/mp.14593 |
0.735 |
|
| 2020 |
Lu C, Bera K, Wang X, Prasanna P, Xu J, Janowczyk A, Beig N, Yang M, Fu P, Lewis J, Choi H, Schmid RA, Berezowska S, Schalper K, Rimm D, et al. A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study. The Lancet. Digital Health. 2: e594-e606. PMID 33163952 DOI: 10.1016/s2589-7500(20)30225-9 |
0.479 |
|
| 2020 |
Shiradkar R, Panda A, Leo P, Janowczyk A, Farre X, Janaki N, Li L, Pahwa S, Mahran A, Buzzy C, Fu P, Elliott R, MacLennan G, Ponsky L, Gulani V, et al. Correction to: T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. European Radiology. PMID 32945970 DOI: 10.1007/s00330-020-07285-8 |
0.413 |
|
| 2020 |
Shiradkar R, Panda A, Leo P, Janowczyk A, Farre X, Janaki N, Li L, Pahwa S, Mahran A, Buzzy C, Fu P, Elliott R, MacLennan G, Ponsky L, Gulani V, et al. T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. European Radiology. PMID 32876839 DOI: 10.1007/S00330-020-07214-9 |
0.476 |
|
| 2020 |
Jayapandian CP, Chen Y, Janowczyk AR, Palmer MB, Cassol CA, Sekulic M, Hodgin JB, Zee J, Hewitt SM, O'Toole J, Toro P, Sedor JR, Barisoni L, Madabhushi A. Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains. Kidney International. PMID 32835732 DOI: 10.1016/J.Kint.2020.07.044 |
0.482 |
|
| 2020 |
Chen Y, Janowczyk A, Madabhushi A. Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis. Jco Clinical Cancer Informatics. 4: 221-233. PMID 32155093 DOI: 10.1200/CCI.19.00068 |
0.551 |
|
| 2020 |
Bhargava HK, Leo P, Elliott R, Janowczyk A, Whitney J, Gupta S, Fu P, Yamoah K, Khani F, Robinson BD, Rebbeck TR, Feldman M, Lal P, Madabhushi A. Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients. Clinical Cancer Research : An Official Journal of the American Association For Cancer Research. PMID 32139401 DOI: 10.1158/1078-0432.Ccr-19-2659 |
0.536 |
|
| 2020 |
Leo* P, Elliott R, Janowczyk A, Janaki N, Bera K, Shiradkar R, El-Fahmawi A, Kim J, Shahait M, Shah A, Thulasidass H, Tewari A, Gupta S, Shih N, Feldman M, et al. PD52-02 COMPUTER-EXTRACTED FEATURES OF GLAND MORPHOLOGY FROM DIGITAL TISSUE IMAGES IS COMPARABLE TO DECIPHER FOR PROGNOSIS OF BIOCHEMICAL RECURRENCE RISK POST-SURGERY The Journal of Urology. 203: e1089-e1090. DOI: 10.1097/Ju.0000000000000954.02 |
0.53 |
|
| 2019 |
Janowczyk A, Zuo R, Gilmore H, Feldman M, Madabhushi A. HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides. Jco Clinical Cancer Informatics. 1-7. PMID 30990737 DOI: 10.1200/CCI.18.00157 |
0.453 |
|
| 2019 |
Leo P, Janowczyk A, Elliott R, Janaki N, Shiradkar R, Farrè X, Yamoah K, Rebbeck T, Shih N, Khani F, Robinson BD, Eklund L, Ettala O, Taimen P, Bostrom P, et al. Computerized histomorphometric features of glandular architecture predict risk of biochemical recurrence following radical prostatectomy: A multisite study. Journal of Clinical Oncology. 37: 5060-5060. DOI: 10.1200/Jco.2019.37.15_Suppl.5060 |
0.559 |
|
| 2018 |
Lu C, Romo-Bucheli D, Wang X, Janowczyk A, Ganesan S, Gilmore H, Rimm D, Madabhushi A. Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers. Laboratory Investigation; a Journal of Technical Methods and Pathology. PMID 29959421 DOI: 10.1038/S41374-018-0095-7 |
0.577 |
|
| 2018 |
Whitney J, Corredor G, Janowczyk A, Ganesan S, Doyle S, Tomaszewski J, Feldman M, Gilmore H, Madabhushi A. Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer. Bmc Cancer. 18: 610. PMID 29848291 DOI: 10.1186/S12885-018-4448-9 |
0.622 |
|
| 2018 |
Janowczyk A, Doyle S, Gilmore H, Madabhushi A. A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images. Computer Methods in Biomechanics and Biomedical Engineering. Imaging & Visualization. 6: 270-276. PMID 29732269 DOI: 10.1080/21681163.2016.1141063 |
0.701 |
|
| 2018 |
Nirschl JJ, Janowczyk A, Peyster EG, Frank R, Margulies KB, Feldman MD, Madabhushi A. A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue. Plos One. 13: e0192726. PMID 29614076 DOI: 10.1371/Journal.Pone.0192726 |
0.735 |
|
| 2018 |
Braman N, Ravichandran K, Janowczyk A, Abraham J, Madabhushi A. Predicting neo-adjuvant chemotherapy response from pre-treatment breast MRI using machine learning and HER2 status. Journal of Clinical Oncology. 36: 582-582. DOI: 10.1200/Jco.2018.36.15_Suppl.582 |
0.545 |
|
| 2018 |
Bhargava HK, Leo P, Elliott R, Janowczyk A, Whitney J, Gupta S, Yamoah K, Rebbeck T, Feldman MD, Lal P, Madabhushi A. Computer-extracted stromal features of African-Americans versus Caucasians from H&E slides and impact on prognosis of biochemical recurrence. Journal of Clinical Oncology. 36: 12075-12075. DOI: 10.1200/Jco.2018.36.15_Suppl.12075 |
0.485 |
|
| 2018 |
Whitney J, Romeo-Bucheli D, Janowczyk A, Ganesan S, Feldman M, Gilmore H, Madabhushi A. Abstract P4-09-11: Computer extracted features of tumor grade from H&E images predict oncotype DX risk categories for early stage ER+ breast cancer Cancer Research. 78. DOI: 10.1158/1538-7445.Sabcs17-P4-09-11 |
0.558 |
|
| 2018 |
Leo P, Shankar E, Elliott R, Janowczyk A, Janaki N, MacLennan GT, Madabhushi A, Gupta S. Abstract LB-021: Combination of quantitative histomorphometry with NFκB/p65 nuclear localization is better predictor of biochemical recurrence in prostate cancer patients Cancer Research. 78. DOI: 10.1158/1538-7445.Am2018-Lb-021 |
0.562 |
|
| 2018 |
Li H, Leo P, Nezami B, Akgul M, Elliott R, Harper H, Janowczyk A, MacLennan G, Madabhushi A. MP08-16 COMBINATION OF NUCLEAR ORIENTATION AND SHAPE FEATURES IN H&E STAINED IMAGES DISTINGUISH CONSENSUS LOW AND HIGH GRADE BLADDER CANCER Journal of Urology. 199. DOI: 10.1016/J.Juro.2018.02.321 |
0.571 |
|
| 2018 |
Leo P, Shankar E, Elliott R, Janowczyk A, Janaki N, MacLennan G, Madabhushi A, Gupta S. MP35-09 COMBINATION OF NF-κB/P65 NUCLEAR LOCALIZATION AND GLAND MORPHOLOGIC FEATURES IS PREDICTIVE OF BIOCHEMICAL RECURRENCE Journal of Urology. 199. DOI: 10.1016/J.Juro.2018.02.1122 |
0.467 |
|
| 2017 |
Wang X, Janowczyk A, Zhou Y, Thawani R, Fu P, Schalper K, Velcheti V, Madabhushi A. Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images. Scientific Reports. 7: 13543. PMID 29051570 DOI: 10.1038/S41598-017-13773-7 |
0.547 |
|
| 2017 |
Lu C, Lewis JS, Dupont WD, Plummer WD, Janowczyk A, Madabhushi A. An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival. Modern Pathology : An Official Journal of the United States and Canadian Academy of Pathology, Inc. PMID 28776575 DOI: 10.1038/Modpathol.2017.98 |
0.573 |
|
| 2017 |
Romo-Bucheli D, Janowczyk A, Gilmore H, Romero E, Madabhushi A. A deep learning based strategy for identifying and associating mitotic activity with gene expression derived risk categories in estrogen receptor positive breast cancers. Cytometry. Part a : the Journal of the International Society For Analytical Cytology. PMID 28192639 DOI: 10.1002/Cyto.A.23065 |
0.555 |
|
| 2016 |
Romo-Bucheli D, Janowczyk A, Gilmore H, Romero E, Madabhushi A. Automated Tubule Nuclei Quantification and Correlation with Oncotype DX risk categories in ER+ Breast Cancer Whole Slide Images. Scientific Reports. 6: 32706. PMID 27599752 DOI: 10.1117/12.2211368 |
0.561 |
|
| 2016 |
Janowczyk A, Madabhushi A. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. Journal of Pathology Informatics. 7: 29. PMID 27563488 DOI: 10.4103/2153-3539.186902 |
0.588 |
|
| 2016 |
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. AutoStitcher: An Automated Program for Efficient and Robust Reconstruction of Digitized Whole Histological Sections from Tissue Fragments. Scientific Reports. 6: 29906. PMID 27457670 DOI: 10.1038/Srep29906 |
0.699 |
|
| 2016 |
Janowczyk A, Basavanhally A, Madabhushi A. Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology. Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society. PMID 27373749 DOI: 10.1016/J.Compmedimag.2016.05.003 |
0.744 |
|
| 2013 |
Janowczyk A, Chandran S, Madabhushi A. Quantifying local heterogeneity via morphologic scale: Distinguishing tumoral from stromal regions. Journal of Pathology Informatics. 4: S8. PMID 23766944 DOI: 10.4103/2153-3539.109865 |
0.487 |
|
| 2012 |
Janowczyk A, Chandran S, Singh R, Sasaroli D, Coukos G, Feldman MD, Madabhushi A. High-throughput biomarker segmentation on ovarian cancer tissue microarrays via hierarchical normalized cuts. Ieee Transactions On Bio-Medical Engineering. 59: 1240-52. PMID 22180503 DOI: 10.1109/Tbme.2011.2179546 |
0.579 |
|
| 2011 |
Xu J, Janowczyk A, Chandran S, Madabhushi A. A high-throughput active contour scheme for segmentation of histopathological imagery. Medical Image Analysis. 15: 851-62. PMID 21570336 DOI: 10.1016/J.Media.2011.04.002 |
0.558 |
|
| 2011 |
Janowczyk A, Chandran S, Feldman M, Madabhushi A. Local morphologic scale: Application to segmenting tumor infiltrating lymphocytes in ovarian cancer TMAs Progress in Biomedical Optics and Imaging - Proceedings of Spie. 7962. DOI: 10.1117/12.878415 |
0.523 |
|
| 2010 |
Xu J, Janowczyk A, Chandran S, Madabhushi A. A weighted mean shift, normalized cuts initialized color gradient based geodesic active contour model: Applications to histopathology image segmentation Progress in Biomedical Optics and Imaging - Proceedings of Spie. 7623. DOI: 10.1117/12.845602 |
0.55 |
|
| 2009 |
Janowczyk A, Chandran S, Singh R, Sasaroli D, Coukos G, Feldman MD, Madabhushi A. Hierarchical normalized cuts: unsupervised segmentation of vascular biomarkers from ovarian cancer tissue microarrays. Medical Image Computing and Computer-Assisted Intervention : Miccai ... International Conference On Medical Image Computing and Computer-Assisted Intervention. 12: 230-8. PMID 20425992 DOI: 10.1007/978-3-642-04268-3_29 |
0.49 |
|
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