Hannah Gilmore, MD

Director of Breast Pathology University Hospitals Cleveland Medical Center

Medical School

Case Western Reserve University School of Medicine, Cleveland, O

Residency

Anatomic and Clinical Pathology, Beth Israel Deaconess Medical Center, Boston, MA

Fellowship(s)

Breast Pathology, Beth Israel Deaconess Medical Center, Boston, MA

Area of Expertise

  • Breast Pathology
  • Anatomic Pathology
  • Molecular Diagnostics
  • Digital Pathology
  • Biomarker Expression and Genomic Profiling

Years of Experience

Over 15 years

Certifications & Licensure

  • Pathology – Anatomic & Clinical Pathology

Biography

Dr. Hannah Gilmore is a board-certified anatomic pathologist specializing in breast pathology at University Hospitals Cleveland Medical Center. She also serves as an Associate Professor of Pathology at Case Western Reserve University and is the Division Chief of Anatomic Pathology.

Dr. Gilmore’s research focuses on the pathogenesis of breast tumors and the identification of diagnostic, predictive, and prognostic markers. She has made significant contributions to the field through her publications and innovative changes in the practice of anatomic pathology, emphasizing subspecialization, multidisciplinary care, molecular diagnostics, and digital pathology.

As the College of American Pathologists representative to the National Accreditation Program for Breast Centers, Dr. Gilmore serves as Vice Chair of the Standards and Accreditation Committee. Her work integrates pathologic, radiologic, and genomic data to study cancer biology, with a particular interest in digital imaging.

Professional Memberships

  • College of American Pathologists
  • National Accreditation Program for Breast Centers

Selected Publications

  1. Toro P, et al. Computational pathology reveals unique spatial patterns of immune response in H&E images from COVID-19 autopsies: preliminary findings. JMI. 2021;8(S1).
  2. Xu J, et al. Convolutional neural network initialized active contour model with adaptive ellipse fitting for nuclear segmentation on breast histopathological images. JMI. 2019;6(1).
  3. Romo-Bucheli D, et al. Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images. Proc. SPIE. 2016;9791.

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