Alexej Gossmann
Alexej Gossmann
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Cited by
Cited by
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
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
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
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
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
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
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
Unified tests for fine scale mapping and identifying sparse high-dimensional sequence associations
S Cao, H Qin, A Gossmann, HW Deng, YP Wang
Proceedings of the 6th ACM Conference on Bioinformatics, Computational …, 2015
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
Sequential algorithmic modification with test data reuse
J Feng, G Pennllo, N Petrick, B Sahiner, R Pirracchio, A Gossmann
Uncertainty in Artificial Intelligence, 674-684, 2022
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
Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees
J Feng, A Gossmann, B Sahiner, R Pirracchio
Journal of the American Medical Informatics Association 29 (5), 841-852, 2022
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
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, 8-18, 2020
Considerations in the assessment of machine learning algorithm performance for medical imaging
A Gossmann, B Sahiner, RK Samala, S Wen, KH Cha, N Petrick
Deep Learning for Medical Image Analysis, 473-507, 2024
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
X Sun, N Chen, A Gossmann, Y Xing, C Feistner, E Dorigatt, F Drost, ...
arXiv preprint arXiv:2403.13728, 2024
Towards a Post-Market Monitoring Framework for Machine Learning-based Medical Devices: A case study
J Feng, A Subbaswamy, A Gossmann, H Singh, B Sahiner, MO Kim, ...
arXiv preprint arXiv:2311.11463, 2023
Methodology for Good Machine Learning with Multi‐Omics Data
T Coroller, B Sahiner, A Amatya, A Gossmann, K Karagiannis, C Moloney, ...
Clinical Pharmacology & Therapeutics, 2023
Deep Unsupervised Clustering for Conditional Identification of Subgroups Within a Digital Pathology Image Set
M Sidulova, X Sun, A Gossmann
International Conference on Medical Image Computing and Computer-Assisted …, 2023
Is this model reliable for everyone? Testing for strong calibration
J Feng, A Gossmann, R Pirracchio, N Petrick, G Pennello, B Sahiner
arXiv preprint arXiv:2307.15247, 2023
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