Hi, my name is Alexej Gossmann. Below is a list of my research publications in a reverse chronological order.

  1. Gossmann, A., Pezeshk, A., Wang, Y.-P., & Sahiner, B. (2021). Test Data Reuse for the Evaluation of Continuously Evolving Classification Algorithms Using the Area under the Receiver Operating Characteristic Curve. SIAM Journal on Mathematics of Data Science, 692–714. https://doi.org/10.1137/20M1333110
    :page_facing_up: DOI: 10.1137/20M1333110
    :octocat: Github repository: DIDSR/ThresholdoutAUC
  2. Pennello, G., Sahiner, B., Gossmann, A., & Petrick, N. (2020). 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. Biometrics. https://doi.org/10.1111/biom.13381
    :page_facing_up: DOI: 10.1111/biom.13381
  3. Gossmann, A., Cha, K. H., & Sun, X. (2020). Performance deterioration of deep neural networks for lesion classification in mammography due to distribution shift: an analysis based on artificially created distribution shift. Medical Imaging 2020: Computer-Aided Diagnosis, 11314, 1131404. https://doi.org/10.1117/12.2551346
    :page_facing_up: DOI: 10.1117/12.2551346
  4. Cha, K. H., Gossmann, A., Petrick, N., & Sahiner, B. (2020). Supplementing training with data from a shifted distribution for machine learning classifiers: adding more cases may not always help. Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, 11316, 113160S. https://doi.org/10.1117/12.2550538
    :page_facing_up: DOI: 10.1117/12.2550538
  5. Gossmann, A., Cha, K. H., & Sun, X. (2019, December). Variational inference based assessment of mammographic lesion classification algorithms under distribution shift. Medical Imaging Meets NeurIPS Workshop (MED-NeurIPS) 2019. https://profs.etsmtl.ca/hlombaert/public/medneurips2019/72_CameraReadySubmission_neurips_2019.pdf
  6. Sun, X., Gossmann, A., Wang, Y., & Bischt, B. (2019). Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift. 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 1344–1353. https://doi.org/10.1109/SSCI44817.2019.9002665
    :page_facing_up: DOI: 10.1109/SSCI44817.2019.9002665
    :page_with_curl: arXiv version: 1906.02972.
    :octocat: Github repository: compstat-lmu/paper_2019_variationalResampleDistributionShift
  7. Hosseinzadeh Kassani, P., Gossmann, A., & Wang, Y.-P. (2019). Multimodal Sparse Classifier for Adolescent Brain Age Prediction. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2019.2925710
    :page_facing_up: DOI: 10.1109/JBHI.2019.2925710
  8. Gossmann, A., Zille, P., Calhoun, V., & Wang, Y.-P. (2018). FDR-Corrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics. IEEE Transactions on Medical Imaging, 37(8), 1761–1774. https://doi.org/10.1109/TMI.2018.2815583
    :page_facing_up: DOI: 10.1109/TMI.2018.2815583
    :page_with_curl: arXiv version: 1705.04312.
    :octocat: Github repository: agisga/FDRcorrectedSCCA
  9. Gossmann, A., Cao, S., Brzyski, D., Zhao, L. J., Deng, H. W., & Wang, Y. P. (2018). A sparse regression method for group-wise feature selection with false discovery rate control. IEEE/ACM Transactions on Computational Biology and Bioinformatics / IEEE, ACM, 15(4), 1066–1078. https://doi.org/10.1109/TCBB.2017.2780106
    :page_facing_up: DOI: 10.1109/TCBB.2017.2780106
    :octocat: Github repository: agisga/grpSLOPEMC
  10. Gossmann, A., Pezeshk, A., & Sahiner, B. (2018, March). Test data reuse for evaluation of adaptive machine learning algorithms: over-fitting to a fixed ’test’ dataset and a potential solution. Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment. https://doi.org/10.1117/12.2293818
    :page_facing_up: DOI: 10.1117/12.2293818
  11. Brzyski, D., Gossmann, A., Su, W., & Bogdan, M. (2018). Group SLOPE – Adaptive Selection of Groups of Predictors. Journal of the American Statistical Association, 1–15. https://doi.org/10.1080/01621459.2017.1411269
    :page_facing_up: DOI: 10.1080/01621459.2017.1411269
    :page_with_curl: arXiv version: 1610.04960.
    :octocat: Github repository: agisga/grpSLOPE
    :package: R package (CRAN): https://cran.r-project.org/package=grpSLOPE
  12. Cao, S., Qin, H., Gossmann, A., Deng, H.-W., & Wang, Y.-P. (2016). Unified tests for fine scale mapping and identifying sparse high-dimensional sequence associations. Bioinformatics, 32(3), 330–337. https://doi.org/10.1093/bioinformatics/btv586
    :page_facing_up: DOI: 10.1093/bioinformatics/btv586
  13. Sammarco, M. C., Simkin, J., Cammack, A. J., Fassler, D., Gossmann, A., Marrero, L., Lacey, M., Van Meter, K., & Muneoka, K. (2015). Hyperbaric Oxygen Promotes Proximal Bone Regeneration and Organized Collagen Composition during Digit Regeneration. PloS One, 10(10). https://doi.org/10.1371/journal.pone.0140156
    :page_facing_up: DOI: 10.1371/journal.pone.0140156
  14. Cao, S., Qin, H., Gossmann, A., Deng, H.-W., & Wang, Y.-P. (2015). Unified Tests for Fine Scale Mapping and Identifying Sparse High-dimensional Sequence Associations. Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, 241–249. https://doi.org/10.1145/2808719.2808744
    :page_facing_up: DOI: 10.1145/2808719.2808744
  15. Gossmann, A., Cao, S., & Wang, Y.-P. (2015). Identification of Significant Genetic Variants via SLOPE, and Its Extension to Group SLOPE. Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, 232–240. https://doi.org/10.1145/2808719.2808743
    :page_facing_up: DOI: 10.1145/2808719.2808743
  16. Gossmann, A. (2012). On disjunction and numerical existence properties of extensions of Heyting arithmetic [Bachelor Thesis]. Technische Universität Darmstadt.
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