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

  1. Sun, X., Feistner, C., Gossmann, A., Schwarz, G., Umer, R. M., Beer, L., Rockenschaub, P., Shrestha, R. B., Gruber, A., Chen, N., Boushehri, S. S., Buettner, F., & Marr, C. (2024). DomainLab: A Modular Python Package for Domain Generalization in Deep Learning.
    :page_with_curl: arXiv version: 2403.14356.
    :file_folder: Github repository: marrlab/DomainLab
  2. Sun, X., Chen, N., Gossmann, A., Xing, Y., Feistner, C., Dorigatt, E., Drost, F., Scarcella, D., Beer, L., & Marr, C. (2024). M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling.
    :page_with_curl: arXiv version: 2403.13728.
  3. Feng, J., Singh, H., Xia, F., Subbaswamy, A., & Gossmann, A. (2024). A Hierarchical Decomposition for Explaining ML Performance Discrepancies.
    :page_with_curl: arXiv version: 2402.14254.
  4. Feng, J., Subbaswamy, A., Gossmann, A., Singh, H., Sahiner, B., Kim, M.-O., Pennello, G., Petrick, N., Pirracchio, R., & Xia, F. (2023). Towards a Post-Market Monitoring Framework for Machine Learning-Based Medical Devices: A Case Study. NeurIPS 2023 Workshop on Regulatable ML. https://openreview.net/forum?id=L97dqPfQdT
    :page_with_curl: arXiv version: 2311.11463.
  5. Gossmann, A., Sahiner, B., Samala, R. K., Wen, S., Cha, K. H., & Petrick, N. (2023). Considerations in the Assessment of Machine Learning Algorithm Performance for Medical Imaging. In K. S. Zhou, H. Greenspan, & D. Shen (Eds.), Deep Learning for Medical Image Analysis (2nd ed., pp. 473–507). Elsevier Academic Press. https://doi.org/10.1016/B978-0-32-385124-4.00029-5
    :page_facing_up: DOI: 10.1016/B978-0-32-385124-4.00029-5
  6. Coroller, T., Sahiner, B., Amatya, A., Gossmann, A., Karagiannis, K., Moloney, C., Samala, R. K., Santana‐Quintero, L., Solovieff, N., Wang, C., Amiri‐Kordestani, L., Cao, Q., Cha, K. H., Charlab, R., Cross Jr, F. H., Hu, T., Huang, R., Kraft, J., Krusche, P., … Zuber, E. (2023). Methodology for Good Machine Learning with Multi‐omics Data. Clinical Pharmacology & Therapeutics, cpt.3105. https://doi.org/10.1002/cpt.3105
    :page_facing_up: DOI: 10.1002/cpt.3105
  7. Sidulova, M., Sun, X., & Gossmann, A. (2023). Deep Unsupervised Clustering for Conditional Identification of Subgroups Within a Digital Pathology Image Set. In H. Greenspan, A. Madabhushi, P. Mousavi, S. Salcudean, J. Duncan, T. Syeda-Mahmood, & R. Taylor (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (Vol. 14227, pp. 666–675). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43993-3_64
    :page_facing_up: DOI: 10.1007/978-3-031-43993-3_64
    :file_folder: Github repository: DIDSR/DomId
  8. Coroller, T., Sahiner, B., Amatya, A., Gossmann, A., Karagiannis, K., Samala, R. K., Santana-Quintero, L., Solovieff, N., Wang, C., Amiri-Kordestani, L., Cao, Q., Cha, K. H., Orbach, R. C., Cross, F. H., Hu, T., Huang, R., Kraft, J., Krusche, P., Li, Y., … Zuber, E. (2023). Multi-Omics Investigation on the Prognostic and Predictive Factors in Metastatic Breast Cancer Using Data from Phase III Ribociclib Clinical Trials: A Statistical and Machine Learning Analysis Plan (p. 2023.08.30.23294367). medRxiv. https://doi.org/10.1101/2023.08.30.23294367
    :page_facing_up: DOI: 10.1101/2023.08.30.23294367
  9. Feng, J., Gossmann, A., Pirracchio, R., Petrick, N., Pennello, G., & Sahiner, B. (2023). Is This Model Reliable for Everyone? Testing for Strong Calibration (Number arXiv:2307.15247). arXiv.
    :page_with_curl: arXiv version: arXiv:2307.15247.
    :file_folder: Github repository: jjfeng/testing_strong_calibration
  10. Feng, J., Gossmann, A., Pennello, G., Petrick, N., Sahiner, B., & Pirracchio, R. (2022). Monitoring Machine Learning (ML)-Based Risk Prediction Algorithms in the Presence of Confounding Medical Interventions (Number arXiv:2211.09781). arXiv.
    :page_with_curl: arXiv version: 2211.09781.
  11. Feng, J., Pennllo, G., Petrick, N., Sahiner, B., Pirracchio, R., & Gossmann, A. (2022). Sequential Algorithmic Modification with Test Data Reuse. Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, 674–684.
    :page_with_curl: arXiv version: 2203.11377.
    :file_folder: Github repository: jjfeng/adaptive_SRGP
  12. Feng, J., Gossmann, A., Sahiner, B., & Pirracchio, R. (2022). Bayesian Logistic Regression for Online Recalibration and Revision of Risk Prediction Models with Performance Guarantees. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocab280
    :page_facing_up: DOI: 10.1093/jamia/ocab280
    :page_with_curl: arXiv version: 2110.06866.
    :file_folder: Github repository: jjfeng/bayesian_model_revision
  13. 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
    :file_folder: Github repository: DIDSR/ThresholdoutAUC
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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.
    :file_folder: Github repository: compstat-lmu/paper_2019_variationalResampleDistributionShift
  19. 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
  20. 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.
    :file_folder: Github repository: agisga/FDRcorrectedSCCA
  21. 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
    :file_folder: Github repository: agisga/grpSLOPEMC
  22. 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
  23. 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.
    :file_folder: Github repository: agisga/grpSLOPE
    :package: R package (CRAN): https://cran.r-project.org/package=grpSLOPE
  24. 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
  25. 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
  26. 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
  27. 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
  28. Gossmann, A. (2012). On disjunction and numerical existence properties of extensions of Heyting arithmetic [Bachelor Thesis]. Technische Universität Darmstadt.
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