| 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 | 27 | 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 | 22 | 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 | 12 | 2018 |
| 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 | 9 | 2016 |
| 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 | 8 | 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 | 7 | 2018 |
| 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 | 4 | 2017 |
| 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 | 1 | 2019 |
| 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, 2020 | | 2020 |
| 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 |
