Erich Bremer

Erich Bremer

Director, Applied Informatics

HSC T3-119 | 631-444-3560 | Erich.Bremer@stonybrook.edu

 

Interests

Linked Data, Artificial Intelligence and Machine Learning, Imaging, 3D graphics and modelling, Pathology Informatics

Selected References

  1. Zhang Y, Abousamra S, Hasan M, Torre-Healy L, Krichevsky S, Shrestha S, et al. Pathomics tumor-infiltrating lymphocytes maps in colorectal cancer for translational immuno-oncology. Zenodo. 2026. doi:10.5281/zenodo.21985178.
  2. Sefton P, Carragáin EÓ, Soiland‐Reyes S, Corcho Ó, Garijo D, Palma R, et al. RO-Crate Metadata Specification 1.3.0. HAL. 2026. doi:10.5281/zenodo.3406497.
  3. Linked web storage protocol 1.0 W3C working draft. World Wide Web Consortium. 2026.
  4. Practical uses of solid for medical and health data. 2026.
  5. BeakGraph: read-optimized RDF Graph store based on HDT/HDF5. 2026.
  6. Gaston SM, Zhang Y, Wallaengen V, Galvez A, Salcedo L, Breto A, et al. Abstract 691: Visualizing differential prostate cancer lesion growth in longitudinal MRIs of patients on active surveillance: Quantitative mapping by Habitat Risk Score and digital pathology. Cancer Research. 2026;86(7_Supplement):691. doi:10.1158/1538-7445.am2026-691.
  7. Bridge C, Abousamra S, Saltz J, Gupta R, Kurc T, Zhang Y, et al. TCGA-SBU-TIL-Maps: AI-derived tumor infiltrating lymphocyte maps for the TCGA collections. Zenodo. 2025. doi:10.5281/zenodo.16966285.
  8. Bhawsar P, Bremer E, Duggan MA, Chanock SJ, García‐Closas M, Saltz J, et al. Tile serving without a server to traverse whole slide imaging data on the web. Research Square. Preprint posted online October 21, 2025. doi:10.21203/rs.3.rs-6256330/v1.
  9. dcm2rdf: Extracting metadata from DICOM medical-imaging files and representing it as RDF. 2025.
  10. Zhang Y, Gupta R, Saltz J, Kurç T, Kaczmarzyk J, Bremer E, et al. Abstract 5315: Mapping tumor infiltrating lymphocytes in whole prostatectomy specimens to visualize and quantify the immune microenvironment of prostate cancer. Cancer Research. 2025;85(8_Supplement_1):5315. doi:10.1158/1538-7445.am2025-5315.
  11. Zhang Y, Abousamra S, Hasan M, Torre-Healy LA, Krichevsky S, Shrestha S, et al. Pathomics image analysis of tumor infiltrating lymphocytes (TILs) in colon cancer. Research Square. Preprint posted online April 1, 2025. doi:10.21203/rs.3.rs-6173056/v1.
  12. Baid U, Pati S, Kurç T, Gupta R, Bremer E, Abousamra S, et al. Pan-Cancer tumor infiltrating lymphocyte detection based on federated learning. In: 2024 IEEE International Conference on Big Data. IEEE; 2024:7640-7647. doi:10.1109/bigdata62323.2024.10825083.
  13. Baghal A, Saltz J, Kurc T, Prasanna P, Baghal S, Hajagos J, et al. Linking. Learn Health Syst. 2024;9(1):e10457. doi:10.1002/lrh2.10457.
  14. Foran DJ, Chen W, Kurc T, Gupta R, Kaczmarzyk JR, Torre-Healy LA, et al. An intelligent search & retrieval system (IRIS) and clinical and research repository for decision support based on machine learning and joint kernel-based supervised hashing. Cancer Inform. 2024;23:11769351231223806. doi:10.1177/11769351231223806.
  15. Kaczmarzyk JR, O'Callaghan A, Inglis F, Gat S, Kurc T, Gupta R, et al. Open and reusable deep learning for pathology with WSInfer and QuPath. NPJ Precis Oncol. 2024;8(1):9. doi:10.1038/s41698-024-00499-9.
  16. Bhawsar P, Bremer E, Duggan MA, Chanock SJ, García‐Closas M, Saltz J, et al. ImageBox3: No-Server tile serving to traverse whole slide images on the web. arXiv. Preprint posted online May 10, 2023. doi:10.21203/rs.3.rs-2864977/v1.
  17. Bremer E, DiPrima T, Balsamo J, Almeida JS, Gupta R, Saltz J. Halcyon -- a pathology imaging and feature analysis and management system. arXiv. 2023. doi:10.48550/ARXIV.2304.10612.
  18. Baid U, Pati S, Kurç T, Gupta R, Bremer E, Abousamra S, et al. Federated learning for the classification of tumor infiltrating lymphocytes. arXiv. Preprint posted online March 30, 2022. doi:10.48550/arxiv.2203.16622.
  19. Foran DJ, Durbin EB, Chen W, Sadimin E, Sharma A, Banerjee I, et al. An expandable informatics framework for enhancing central cancer registries with digital pathology specimens, computational imaging tools, and advanced mining capabilities. J Pathol Inform. 2022;13:5. doi:10.4103/jpi.jpi_31_21.
  20. Bremer E, Saltz J, Almeida JS. ImageBox 2 - efficient and rapid access of image tiles from Whole-Slide images using serverless HTTP range requests. J Pathol Inform. 2020;11(1):29. doi:10.4103/jpi.jpi_31_20.
  21. Sharma A, Tarbox L, Kurc T, Bona J, Smith K, Kathiravelu P, et al. PRISM: A platform for imaging in precision medicine. JCO Clin Cancer Inform. 2020;4(4):491-499. doi:10.1200/cci.20.00001.
  22. Bremer E, Almeida JS, Saltz J. Representing whole slide cancer image features with hilbert curves. arXiv. Preprint posted online May 13, 2020. doi:10.48550/arxiv.2005.06469.
  23. Le H, Gupta R, Hou L, Abousamra S, Fassler D, Torre-Healy L, et al. Utilizing automated breast cancer detection to identify spatial distributions of tumor infiltrating lymphocytes in invasive breast cancer. Am J Pathol. 2020;190(7):1491-1504. doi:10.1016/j.ajpath.2020.03.012.
  24. Kurç T, Sharma A, Gupta R, Hou L, Le H, Abousamra S, et al. From whole slide tissues to knowledge: Mapping sub-cellular morphology of cancer. In: Lecture notes in computer science. Springer Science+Business Media; 2020:371-379. doi:10.1007/978-3-030-46643-5_37.
  25. Sefton P, Carragáin EÓ, Soiland‐Reyes S, Corcho Ó, Garijo D, Palma R, et al. RO-Crate Metadata Specification 1.0. Research Explorer. 2019. doi:10.5281/zenodo.20720080.
  26. Taveira LFR, Kurc T, Melo ACMA, Kong J, Bremer E, Saltz JH, et al. Multi-objective parameter auto-tuning for tissue image segmentation workflows. J Digit Imaging. 2019;32(3):521-533. doi:10.1007/s10278-018-0138-z.
  27. Taveira LFR, Kurç T, de Melo ACMA, Kong J, Bremer E, Saltz J, et al. Tuning for tissue image segmentation workflows for accuracy and performance. arXiv. Preprint posted online October 5, 2018. doi:10.48550/arxiv.1810.02911.
  28. Saltz J, Sharma A, Iyer G, Bremer E, Wang F, Jasniewski A, et al. A containerized software system for generation, management, and exploration of features from whole slide tissue images. Cancer Res. 2017;77(21):e79-e82. doi:10.1158/0008-5472.can-17-0316.
  29. Saltz J, Almeida J, Gao Y, Sharma A, Bremer E, DiPrima T, et al. Towards generation, management, and exploration of combined radiomics and pathomics datasets for cancer research. AMIA Jt Summits Transl Sci Proc. 2017;2017:85-94. PMID 28815113.
  30. Bremer E, Kurc T, Gao Y, Saltz J, Almeida JS. Safe "cloudification" of large images through picker APIs. AMIA Annu Symp Proc. 2016;2016:342-351. PMID 28269829.
  31. Saltz M, Saltz J, Hajagos J, White AD, Boicey C, Murry J, et al. Integrative informatics and predictive modeling support for population health. AMIA. 2015.
  32. Torniai C, Essaid S, Barnes C, Conlon M, Williams SF, Hajagos J, et al. From EHRs to Linked Data: representing and mining encounter data for clinical expertise evaluation. 2013;2013:165. PMID 24303330.
  33. Basu S, Bremer E, Zhou C, Bogenhagen DF. MiGenes: a searchable interspecies database of mitochondrial proteins curated using gene ontology annotation. Bioinformatics. 2005;22(4):485-92. doi:10.1093/bioinformatics/btk009.