The grant is entitled “Integrating omics and quantitative imaging data in co-clinical trials to predict treatment response in triple negative breast cancer.” The ePAD technology (http://epad.stanford.edu) developed by the QIAI Lab is central to this grant by bringing quantitative image analysis methods to small animal imaging and unifying quantitative assessment of cancer on images in both animal and human studies to catalyze research in the co-clinical trials paradigm. The Rubin Lab is also affiliated with the Stanford Center for Biomedical Informatics Research (formally Stanford Medical Informatics or SMI). The NSF-funded LSST (now Rubin Observatory) Project Office for construction was established as an operating center under management of the Association of Universities for Research in Astronomy (AURA). Jorie Singer. Our ultimate goal is to bridge the divide between radiological knowledge and practice--for all radiological knowledge and research data to be structured, accessed, and processed by computers so that we can create and deploy decision support applications in image workstations to improve radiologist clinical effectiveness. Consequently, radiologists and clini- ... * Daniel L. Rubin dlrubin@stanford.edu Selen Bozkurt selenb@stanford.edu Emel Alkim ealkim@stanford.edu Imon … We also develop tools to efficiently and thoroughly capture the semantic terms radiologists use to describe lesions; standardized terminologies to enable radiologists to describe lesions comprehensively and consistently; image processing methods to characterize lesions; content-based image retrieval with structured image information to enable radiologists to find similar images; methods to enable physicians to quantitatively and reproducibly assess tumor burden in images and to more effectively monitor treatment response in cancer treatment; natural language techniques to enable uniform indexing, searching, and retrieval of radiology information resources such as radiology reports; and decision support applications that relate radiology findings to diagnoses to improve diagnostic accuracy. Stanford, CA 94305-5479 A recent focus of the lab is in deep learning methods for automated image classification, lesion detetction, segmentation, and clinical prediction. Clinical Research Coordinator. Just as biology has been revolutionized by online genetic data, our goal is to advance radiology by making the content in images and medical texts computable and to electronically correlate images and texts with other clinical data such as pathology and molecular data. Our research group uses artificial intelligence (AI) and computational methods to leverage the information in radiology images to enable biomedical discovery and to guide physicians in personalized care. We are developing novel methods to tackle recent challenges in AI related to limited quality labeled data, including weak learning, multi-task learning, and multi-modal models. Our ultimate goal is to bridge the divide between radiological knowledge and practice--for all radiological knowledge and research data to be structured, accessed, and processed by computers so that we can create and deploy decision support applications in image workstations to improve radiologist clinical effectiveness. The site facilitates research and collaboration in academic endeavors. The ePAD technology (http://epad.stanford.edu) that the Rubin lab has been developing is central to this grant, bringing quantitative image analysis methods to small animal imaging and unifying quantitative assessment of cancer on images in both animal and human studies to catalyze research in the co-clinical trials paradigm. Yoni Samuel Rubin, PhD is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). V. Akondi and A. Dubra, Opt. Clinical Research Coordinator. Semi-automatic geographic atrophy segmentation for SD-OCT images Qiang Chen,1,2,* Luis de Sisternes,2 Theodore Leng, 3 Luoluo Zheng, Lauren Kutzscher,3 and Daniel L. Rubin2,4 1 School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China 2 Department of Radiology and Medicine (Biomedical Informatics Research), Stanford University, Stanford… Prediction of EGFR and KRAS mutation in non-small cell lung cancer using quantitative (18)F FDG-PET/CT metrics. STARR Cohort Discovery Stanford Rubin’s practice centers on estate and tax planning and trust and probate administration matters for individuals of substantial net worth. Support teaching, research, and patient care. Eun-Ju Chang, Ph.D. Current Position: Assistant Professor, Department of Anatomy & Cell Biology, University of Ulsan College of Medicine, Seoul, Korea Contact: cej1103@yahoo.com SLAC National Accelerator Laboratory - Vera C. Rubin Observatory LSST Camera Learn how we are healing patients through science & compassion, Stanford team stimulates neurons to induce particular perceptions in mice's minds, Students from far and near begin medical studies at Stanford. and Daniel L. Rubin2,4 1 School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China 2 Department of Radiology and Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA 94305, USA Rubin Observatory is a federal project jointly funded by the National Science Foundation and the Department of Energy, with early construction funding received from private donations through the LSST Corporation. 1265 Welch Road, Room X-335, MC 5464  Dr. Rubin founded Stanford's 3D Imaging laboratory serving as its Medical Director and led the section of Cardiovascular Imaging. Our work develops and translates basic biomedical informatics methods to improve radiology practice and decision making in several areas to enable precision healthcare. We are also focusing on tackling the challenge of making clinical predictions on longitudinal image and text data and we have several important recent advances in predicting patient survival and disease progression. We are pleased to announce that ePAD project (http://epad.stanford.edu) of the QIAI Lab, "ePAD: A platform to enable machine learning and AI application development in medical imaging" (B-0906) has been selected to receive the Best Scientific Paper Presentation Award at the 2019 European Congress of Radiology in Imaging Informatics, https://www.myesr.org/congress, Medical School Office Building (MSOB) Lee Rubin, Ph.D. Our laboratory is broadly interested in the mechanisms underlying changes in the nervous system as a result of aging or disease, as well as the interactions between the nervous system and the rest of the body that mediate health versus disease. A recent focus of the lab is in deep learning methods for automated image classification, lesion detetction, segmentation, and clinical prediction. The objectives of her laboratory research are to identify specific inflammatory pathways that may be targeted to prevent and treat neurodegenerative disorders such as Parkinson’s disease and Alzheimer’s disease.

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