Louis J Vaickus, MD, PhD
Title(s):
Assistant 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
Selected Publications: |
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Using molecular testing to improve the management of thyroid nodules with indeterminate cytology: an institutional experience with review of molecular alterations. MethylSPWNet and MethylCapsNet: Biologically Motivated Organization of DNAm Neural Networks, Inspired by Capsule Networks. Atypia of undetermined significance in thyroid cytology: Nuclear atypia and architectural atypia are associated with different molecular alterations and risks of malignancy. A large-scale internal validation study of unsupervised virtual trichrome staining technologies on nonalcoholic steatohepatitis liver biopsies. Fine needle aspiration of an intranodal follicular dendritic cell sarcoma: A case report with molecular analysis and review of the literature. Evaluation of a Deep Neural Network for Automated Classification of Colorectal Polyps on Histopathologic Slides. A machine learning algorithm for simulating immunohistochemistry: development of SOX10 virtual IHC and evaluation on primarily melanocytic neoplasms. PathFlowAI: A High-Throughput Workflow for Preprocessing, Deep Learning and Interpretation in Digital Pathology. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Automating the Paris System for urine cytopathology-A hybrid deep-learning and morphometric approach. |
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