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
Academic Analytics
View Profile
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
Use of molecular testing results to analyze the overuse of atypia of undetermined significance in thyroid cytology. Assessment of emerging pretraining strategies in interpretable multimodal deep learning for cancer prognostication. Paired-agent imaging as a rapid en face margin screening method in Mohs micrographic surgery. Large-scale validation study of an improved semiautonomous urine cytology assessment tool: AutoParis-X. Examining longitudinal markers of bladder cancer recurrence through a semiautonomous machine learning system for quantifying specimen atypia from urine cytology. A deep learning algorithm to detect cutaneous squamous cell carcinoma on frozen sections in Mohs micrographic surgery: a retrospective assessment. Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: A pilot study. Identification of Spatial Proteomic Signatures of Colon Tumor Metastasis: A Digital Spatial Profiling Approach. Video-Based Deep Learning to Detect Dyssynergic Defecation with 3D High-Definition Anorectal Manometry. Large-scale longitudinal comparison of urine cytological classification systems reveals potential early adoption of The Paris System criteria. |