David F Steiner
David F Steiner
Molecular Pathology, Stanford University
Verified email at
Cited by
Cited by
Determining cell type abundance and expression from bulk tissues with digital cytometry
AM Newman, CB Steen, CL Liu, AJ Gentles, AA Chaudhuri, F Scherer, ...
Nature biotechnology 37 (7), 773-782, 2019
MicroRNA-29 regulates T-box transcription factors and interferon-γ production in helper T cells
DF Steiner, MF Thomas, JK Hu, Z Yang, JE Babiarz, CDC Allen, ...
Immunity 35 (2), 169-181, 2011
Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer
DF Steiner, R MacDonald, Y Liu, P Truszkowski, JD Hipp, C Gammage, ...
The American journal of surgical pathology 42 (12), 1636, 2018
Antigen presentation profiling reveals recognition of lymphoma immunoglobulin neoantigens
MS Khodadoust, N Olsson, LE Wagar, OAW Haabeth, B Chen, ...
Nature 543 (7647), 723-727, 2017
" Hello AI": uncovering the onboarding needs of medical practitioners for human-AI collaborative decision-making
CJ Cai, S Winter, D Steiner, L Wilcox, M Terry
Proceedings of the ACM on Human-computer Interaction 3 (CSCW), 1-24, 2019
Chest radiograph interpretation with deep learning models: assessment with radiologist-adjudicated reference standards and population-adjusted evaluation
A Majkowska, S Mittal, DF Steiner, JJ Reicher, SM McKinney, GE Duggan, ...
Radiology 294 (2), 421-431, 2020
Distinct requirements of microRNAs in NK cell activation, survival, and function
NA Bezman, E Cedars, DF Steiner, R Blelloch, DGT Hesslein, LL Lanier
The Journal of Immunology 185 (7), 3835-3846, 2010
MicroRNAs 24 and 27 suppress allergic inflammation and target a network of regulators of T helper 2 cell-associated cytokine production
HH Pua, DF Steiner, S Patel, JR Gonzalez, JF Ortiz-Carpena, ...
Immunity 44 (4), 821-832, 2016
Deep learning-based survival prediction for multiple cancer types using histopathology images
E Wulczyn, DF Steiner, Z Xu, A Sadhwani, H Wang, I Flament-Auvigne, ...
PloS one 15 (6), e0233678, 2020
Development and validation of a deep learning algorithm for Gleason grading of prostate cancer from biopsy specimens
K Nagpal, D Foote, F Tan, Y Liu, PHC Chen, DF Steiner, N Manoj, ...
JAMA oncology 6 (9), 1372-1380, 2020
Interpretable survival prediction for colorectal cancer using deep learning
E Wulczyn, DF Steiner, M Moran, M Plass, R Reihs, F Tan, ...
NPJ digital medicine 4 (1), 1-13, 2021
Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
W Bulten, K Kartasalo, PHC Chen, P Ström, H Pinckaers, K Nagpal, Y Cai, ...
Nature medicine 28 (1), 154-163, 2022
Nhp6 is a transcriptional initiation fidelity factor for RNA polymerase III transcription in vitro and in vivo
GA Kassavetis, DF Steiner
Journal of Biological Chemistry 281 (11), 7445-7451, 2006
Evaluation of the use of combined artificial intelligence and pathologist assessment to review and grade prostate biopsies
DF Steiner, K Nagpal, R Sayres, DJ Foote, BD Wedin, A Pearce, CJ Cai, ...
JAMA network open 3 (11), e2023267-e2023267, 2020
Complete and prolonged response to immune checkpoint blockade in POLE-mutated colorectal cancer
R Silberman, D F Steiner, AA Lo, A Gomez, JL Zehnder, G Chu, ...
American Society of Clinical Oncology (ASCO), 2019
Determining breast cancer biomarker status and associated morphological features using deep learning
P Gamble, R Jaroensri, H Wang, F Tan, M Moran, T Brown, ...
Communications medicine 1 (1), 1-12, 2021
Closing the translation gap: AI applications in digital pathology
DF Steiner, PHC Chen, CH Mermel
Biochimica et Biophysica Acta (BBA)-Reviews on Cancer 1875 (1), 188452, 2021
Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading
E Wulczyn, K Nagpal, M Symonds, M Moran, M Plass, R Reihs, F Nader, ...
Communications medicine 1 (1), 1-8, 2021
High-throughput sequencing of subcutaneous panniculitis-like T-cell lymphoma reveals candidate pathogenic mutations
S Fernandez-Pol, HA Costa, DF Steiner, L Ma, JD Merker, YH Kim, ...
Applied Immunohistochemistry & Molecular Morphology 27 (10), 740-748, 2019
Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images
A Sadhwani, HW Chang, A Behrooz, T Brown, I Auvigne-Flament, H Patel, ...
Scientific reports 11 (1), 1-11, 2021
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