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
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. Artificial Intelligence Applications in Cytopathology: Current State of the Art. Thyroid Fine-Needle Aspiration: The Current and Future Landscape of Cytopathology. Intraoperative margin assessment for basal cell carcinoma with deep learning and histologic tumor mapping to surgical site. Correction: A2B Adenosine Receptor Expression by Myeloid Cells is Proinflammatory in Murine Allergic-Airway Inflammation. Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining. Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis. |
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