Alexej Gossmann
Alexej Gossmann
Dirección de correo verificada de fda.hhs.gov - Página principal
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Hyperbaric oxygen promotes proximal bone regeneration and organized collagen composition during digit regeneration
MC Sammarco, J Simkin, AJ Cammack, D Fassler, A Gossmann, ...
PloS one 10 (10), e0140156, 2015
342015
Group slope–adaptive selection of groups of predictors
D Brzyski, A Gossmann, W Su, M Bogdan
Journal of the American Statistical Association 114 (525), 419-433, 2019
282019
FDR-corrected sparse canonical correlation analysis with applications to imaging genomics
A Gossmann, P Zille, V Calhoun, YP Wang
IEEE transactions on medical imaging 37 (8), 1761-1774, 2018
162018
Unified tests for fine-scale mapping and identifying sparse high-dimensional sequence associations
S Cao, H Qin, A Gossmann, HW Deng, YP Wang
Bioinformatics 32 (3), 330-337, 2016
112016
Test data reuse for evaluation of adaptive machine learning algorithms: over-fitting to a fixed'test'dataset and a potential solution
A Gossmann, A Pezeshk, B Sahiner
Medical Imaging 2018: Image Perception, Observer Performance, and Technology …, 2018
82018
Identification of significant genetic variants via SLOPE, and its extension to group SLOPE
A Gossmann, S Cao, YP Wang
Proceedings of the 6th ACM Conference on Bioinformatics, Computational …, 2015
82015
A sparse regression method for group-wise feature selection with false discovery rate control
A Gossmann, S Cao, D Brzyski, LJ Zhao, HW Deng, YP Wang
IEEE/ACM transactions on computational biology and bioinformatics 15 (4 …, 2017
52017
Variational resampling based assessment of deep neural networks under distribution shift
X Sun, A Gossmann, Y Wang, B Bischt
2019 IEEE Symposium Series on Computational Intelligence (SSCI), 1344-1353, 2019
3*2019
Multimodal sparse classifier for adolescent brain age prediction
PH Kassani, A Gossmann, YP Wang
IEEE journal of biomedical and health informatics 24 (2), 336-344, 2019
32019
Test Data Reuse for the Evaluation of Continuously Evolving Classification Algorithms Using the Area under the Receiver Operating Characteristic Curve
A Gossmann, A Pezeshk, YP Wang, B Sahiner
SIAM Journal on Mathematics of Data Science 3 (2), 692-714, 2021
12021
Discussion on “Approval policies for modifications to machine learning‐based software as a medical device: A study of bio‐creep” by Jean Feng, Scott Emerson, and Noah Simon
G Pennello, B Sahiner, A Gossmann, N Petrick
Biometrics 77 (1), 45-48, 2021
2021
Supplementing training with data from a shifted distribution for machine learning classifiers: adding more cases may not always help
KH Cha, A Gossmann, N Petrick, B Sahiner
Medical Imaging 2020: Image Perception, Observer Performance, and Technology …, 2020
2020
Performance deterioration of deep neural networks for lesion classification in mammography due to distribution shift: an analysis based on artificially created distribution shift
A Gossmann, KH Cha, X Sun
Medical Imaging 2020: Computer-Aided Diagnosis 11314, 1131404, 2020
2020
Variational inference based assessment of mammographic lesion classification algorithms under distribution shift
A Gossmann, KH Cha, X Sun
Medical Imaging Meets NeurIPS Workshop (MED-NeurIPS) 2019, 2019
2019
Regaining Control of False Findings in Feature Selection, Classification, and Prediction on Neuroimaging and Genomics Data
A Gossmann
Tulane University School of Science and Engineering, 2018
2018
Analysis of Bone Growth Data by Mixed-Effects SS ANOVA Methods
A Gossmann
2013
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Artículos 1–16