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

  1. Sidulova, M., Kahaki, S., Hagemann, I., & Gossmann, A. (2024). Contextual Unsupervised Deep Clustering in Digital Pathology. In Proceedings of the Conference on Health, Inference, and Learning (pp. 558–565). Association for Computing Machinery.
    :file_folder: Github repository: DIDSR/DomId
  2. 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
  3. 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.
  4. 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.
  5. Feng, J., Gossmann, A., Pennello, G., Petrick, N., Sahiner, B., & Pirracchio, R. (2024). Monitoring machine learning-based risk prediction algorithms in the presence of performativity . In S. Dasgupta, S. Mandt, & Y. Li (Eds.), Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 919–927). PMLR. https://proceedings.mlr.press/v238/feng24b.html
    :page_with_curl: arXiv version: 2211.09781.
  6. Feng, J., Gossmann, A., Pirracchio, R., Petrick, N., Pennello, G., & Sahiner, B. (2024). Is This Model Reliable for Everyone? Testing for Strong Calibration. In S. Dasgupta, S. Mandt, & Y. Li (Eds.), Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 181–189). PMLR. https://proceedings.mlr.press/v238/feng24a.html
    :page_with_curl: arXiv version: arXiv:2307.15247.
    :file_folder: Github repository: jjfeng/testing_strong_calibration
  7. 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.
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. Gossmann, A. (2012). On disjunction and numerical existence properties of extensions of Heyting arithmetic [Bachelor Thesis]. Technische Universität Darmstadt.
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