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 developed
The Clinical Image Analysis Lab (CIALAB) that was started by Dr. Metin Gurcan in 2005 at the Ohio State University has moved to Wake Forest School of Medicine. The new lab at Wake Forest will retain its name, but its web page will now be located at : http://tsi.wakehealth.edu/CIALab/ Dr. Gurcan was recruited as the inaugural Director of the Center for Biomedical Informatics at Wake Forest School of Medicine. The newly established Center will strengthen Wake Forest’s infrastructures for informatics research in genomics, imaging, healthcare delivery, and population health and support the informatics needs of other research centers. In so doing, the Center will promote Wake Forest Baptist Health's evolution as a learning health care system. A further goal of the Center is to train the next generation of investigators in the principles and practice of biomedical informatics. The CIALab has been one of the leading research labs in the world and known for its innovative and trans