Journal Papers

2018

  • “Quantitative Nuclear Histomorphometry predicts Oncotype DX risk categories for early stage ER+ Breast Cancer,” BMC Cancer (2018) in press. Abstract

2016

  • “A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization (2018) 6(3):270-276. Abstract

2012

  • "Cascaded discrimination of normal, abnormal, and confounder classes in histopathology - Gleason grading of prostate cancer," BMC Bioinformatics (2012) 13:282. Abstract

Conference Papers

2018

  • “Registration parameter optimization for 3D tissue modeling from resected tumors cut into serial H&E slides,” Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810T (2018). Abstract
  • “Role of training data variability on classifier performance and generalizability,” Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810T (2018). Abstract

2016

  • "Quantification of tumor morphology via 3D histology: application to oral cavity cancers," Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 979112 (2016). Abstract

About

I am Assistant Professor of Pathology and Anatomical Sciences, Biomedical Engineering, and Biomedical Informatics at the University at Buffalo, SUNY. My lab develops computational tools for medical data, with a focus on imaging, machine learning, and artificial intelligence.

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