Research

Walsh, Colin G.; Ripperger, Michael A.; Novak, Laurie; Reale, Carrie; Anders, Shilo; Spann, Ashley; Kolli, Jhansi; Robinson, Katelyn; Chen, Qingxia; Isaacs, David; Acosta, Lealani Mae Y.; Phibbs, Fenna; Fielstein, Elliot; Wilimitis, Drew; Musacchio Schafer, Katherine; Hilton, Rachel; Albert, Dan; Shelton, Jill; Stroh, Jessica; Stead, William W.; Johnson, Kevin B. Read More

Trujillo, Paula; O’Rourke, Kaitlyn R.; Roman, Olivia C.; Song, Alexander K.; Hett, Kilian; Cooper, Amy; Black, Bonnie K.; Donahue, Manus J.; Shibao, Cyndya A.; Biaggioni, Italo; Claassen, Daniel O. “Central Involvement in Pure Autonomic Failure: Insights from Neuromelanin-Sensitive Magnetic Resonance Imaging and 18F-Fluorodopa-Positron Emission Tomography.” Movement… Read More

Landman, Bennett. “Reflecting on a Year of Growth and Opportunity.” Journal of Medical Imaging, vol. 11, no. 6, 2024, 60101, https://doi.org/10.1117/1.JMI.11.6.060101.  The editorial reflects on the past year, highlighting impactful research and discussing challenges as the 11th volume of the… Read More

Liu, J., Zhang, Y., Chen, J.-N., Xiao, J., Lu, Y., Landman, B. A., Yuan, Y., Yuille, A., Tang, Y., & Zhou, Z. (2023). CLIP-driven universal model for organ segmentation and tumor detection. In Proceedings of the IEEE International Conference on Computer Vision (pp. 21095–21107). Read More

Andreadis, K., Muellers, K., Ancker, J. S., Horowitz, C., Kaushal, R., & Lin, J. J. (2023). Telemedicine impact on the patient-provider relationship in primary care during the COVID-19 pandemic. Medical Care, 61(4), S83–S88. doi: 10.1097/MLR.0000000000001808  The COVID-19 pandemic made telemedicine, or… Read More

Maier-Hein, L., Reinke, A., Godau, P., et al. (2024). Metrics reloaded: recommendations for image analysis validation. Nature Methods, 21(2), 195-212. doi: 10.1038/s41592-023-02151-z    There is growing evidence that problems with validating machine learning (ML) algorithms are a global issue that’s often overlooked. Read More

Cui, C., Yang, H., Wang, Y., Zhao, S., Asad, Z., Coburn, L. A., Wilson, K. T., Landman, B. A., & Huo, Y. (2023). Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review. Progress in Biomedical Engineering, 5(2), 22001. Read More

Jain, S., Pei, L., Spraggins, J. M., Angelo, M., Carson, J. P., Gehlenborg, N., Ginty, F., Gonçalves, J. P., Hagood, J. S., Hickey, J. W., Kelleher, N. L., Laurent, L. C., Lin, S., Lin, Y., Liu, H., Naba, A., Nakayasu, E. S., Qian, W.-J., Radtke, A., Robson, P., Stockwell,… Read More

Kolouri, S., Naderializadeh, N., Rohde, G. K., & Hoffmann, H. (2021). WASSERSTEIN EMBEDDING FOR GRAPH LEARNING. ICLR 2021 – 9th International Conference on Learning Representations, 34. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150286096&partnerID=40&md5=8b88e110167be2c5fd01da171324d3d6  We introduce a new method called Wasserstein Embedding for Graph Learning (WEGL), which is a… Read More

Russell, R. G., Lovett Novak, L., Patel, M., Garvey, K. V., Craig, K. J. T., Jackson, G. P., Moore, D., & Miller, B. M. (2023). Competencies for the use of artificial intelligence-based tools by health care professionals. Academic Medicine, 98(3), 348-356. doi: 10.1097/ACM.0000000000004963… Read More