Louis J Vaickus, MD, PhD
Title(s)
Associate Professor of Pathology and Laboratory Medicine
Additional Titles/Positions/Affiliations
Medical Director of Pathology Informatics
Department(s)
Pathology and Laboratory Medicine
Education
BA, Hamilton College, Clinton NY, 2001-2005
MD, Boston University School of Medicine, Boston MA, 2005-2012
PhD, Boston University School of Medicine, Boston MA, 2005-2012
Anatomic Pathology Residency, Massachusetts General Hospital, Boston MA, 2012-2016
Cytopathology Fellowship, Massachusetts General Hospital, Boston MA, 2014-2015
Contact Information
1 Medical Center Drive
Lebanon NH 03766
Office: Borwell 4
Phone: 53844
Email: Louis.J.Vaickus@Dartmouth.edu
Professional Interests
Informatics
Deep learning
Medical image analysis
Automation
Programming
Grant Information
SYNERGY Clinical Research Fellow, 2018-2019
An Explanatory Deep Learning Pipeline for the Prediction and Visualization of Spatially Resolved Biomarker Expression in Triple-Negative Breast Cancer. Metastasis-associated DNA methylation alterations persist after accounting for immune and stromal cell heterogeneity in primary colorectal tumors. Adapting transformer-based encoder models for automated billing code assignment of current procedural terminology from pathology reports in multi-institutional settings. Accurate focal-plane selection is crucial for artificial intelligence assessment of three-dimensional urine cytology specimens for bladder cancer screening and surveillance. Interpreting and Validating a Deep Learning Model Predictive of Spatial Morphologic-Molecular Patterns in Lung Adenocarcinoma, Using Ground Truth Immunohistochemistry Images. X-SPATIO: An Explanatory Deep Learning Pipeline for the Prediction and Visualization of Spatially Resolved Biomarker Expression in Triple-Negative Breast Cancer. Seven-year retrospective review of medical microbiologist consultations on cytology specimens at an academic medical center. Association of deep learning-derived histologic features of placental chorionic villi with maternal and infant characteristics in the New Hampshire birth cohort study. Association of Deep Learning-Derived Histologic Features of Placental Chorionic Villi with Maternal and Infant Characteristics in the New Hampshire Birth Cohort Study. An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology. |
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