Drs. Niazi and Akakin are planned to present at the
SPIE Digital Pathology Conference in February in Orlando, FL. Dr. Gurcan will
chair the conference. Dr. Akakin will present the paper “An Adaptive Algorithm
for Detection of Multiple-Type, Positively Stained Nuclei in IHC images with
minimal Prior Information: Application to OLIG2 Staining Glioms” in which she will
discuss the method of segmentation of oligodendrocytes and gliomas in OLIG2
immunoperoxidase stained tissue sections. This project is in collaboration with
Drs. Otero and Gokozan of OSU Pathology Department. Dr. Niazi will present the
paper, “Characterizing primary refractory neuroblastoma: Prediction of outcome
by microscopic image analysis” which involves developing pre-treatment
clinic-biological biomarkers that can guide precise and effective therapeutic
strategies to neuroblastoma. This project is in collaboration with Dr. Weiser
of Albert Einstein College of Medicine and Dr. Pawel of The Children’s Hospital
of Philadelphia. Further details about the conference and papers can be found
at the conference website: http://spie.org/MI/conferencedetails/digital-pathology
The CIA lab has recently had 4 articles published in PLOS One and the Journal of Urology. Automated Staging Of T1 Bladder Cancer Using Digital Pathologic H&E Images: A Deep Learning approach (Journal of Urology). The paper discusses the need for accurately gauging tumor cell intrusion into Lamina Propria in an effort to substage bladder cancer. It explains how transfer learning in conjunction with Convolutional Neural Networks can be used to accurately identify different bladder layers and then compute the distance between tumor nuclei and Lamina Propria. The article is available here: https://www.jurology.com/article/S0022-5347(18)41148-2/pdf Identifying tumor in pancreatic neuroendocrine neoplasms from Ki67 images using transfer learning (PLOS One). This paper examines a proposed methodology to automatically differentiate between NET and non-tumor regions based on images of Ki67 stained biopsies. It also uses transfer learning to exploit a rich set of features ...
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