Dr. Ulas Bagci,
2018/April Our new method SegCaps is out "Capsules for Object Segmentation"
Center for Research in Computer Vision (CRCV), UCF.
2018/April New Paper in British Journal of Radiology "Deep Learning Beyond Cats and Dogs: Recent Advances in Diagnosing Breast Cancer with Deep Neural Networks"
2018/April New Paper in IEEE EMBC 2018 "Semi-Supervised Multi-Task Learning for Lung Cancer Diagnosis"
2018/March New Paper in Medical Image Analysis journal "Joint Solution for PET Image Segmentation, Denoising, and Partial Volume Correcttion" 2018
2017/December Our "Deep Learning for Radiology Applications" study received RSNA Merit Award, 2017
2017/December Our Visual Turing experiment paper "How to Fool Radiologists with GAN" was accepted for IEEE ISBI 2018
2017/December Our Deep Learning based IPMN (Pancreatic Cyst) Diagnosis paper was accepted for IEEE ISBI 2018
2017/September Our kidney and cortex segmentation work appears in Medical Image Analysis Journal
2017/August Harish's work is nominated for BCI 2017 award: Gold Standard for epilepsy/tumor surgery coupled with deep learning offers independence to a promising functional mapping modality
2017/August Our deep learning based retinal fluid detection and quantification paper is accepted to MICCAI-RETOUCH Challenge
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Prof. Bagci is a faculty member at the Center for Research in Computer Vision (CRCV), and the Assistant Professor in University of Central Florida (UCF). His research interests are image processing and statistical machine learning and their applications in biomedical and clinical imaging. Previously, Prof. Bagci was a staff scientist and the lab manager at the NIH's Center for Infectious Disease Imaging (CIDI) Lab, department of Radiology and Imaging Sciences (RAD&IS). At NIH, Prof. Bagci has developed and implemented educational and scientific research initiatives, and mentored postdoctoral and postbaccalaureate fellows for quantitative image analysis in clinical and pre-clinical projects at the Clinical Center. Prof. Bagci had been the leading scientist (image analyst) in biosafety/bioterrorism project initiated jointly by NIAID and IRF. He obtained his PhD degree from School of Computer Science, University of Nottingham (UK) in collaboration with Radiology department of University of Pennsylvania (with Prof. Udupa, MIPG). He has masters from Electrical Engineering and Computer Sciences and certificates of mastery from statistics, public health, and clinical trials fields. Prof. Bagci is senior member of IEEE and RSNA, and member of scientific organizations such as Society of Nuclear Medicine and Molecular Imaging (SNMMI), American Statistical Association (ASA), Royal Statistical Society (RSS), AAAS, and MICCAI. Prof. Bagci has served as a program committee member for various conferences, and a ad-hoc reviewer for many prestigious journals in his fields and received best reviewer awards (most recently MICCAI 2016 Best Scientific Reviewer Award). Prof. Bagci is the recipient of many awards including NIH’s FARE award (twice), RSNA Merit Certificates (4 times), best paper awards, poster prizes, and several highlights in journal covers, media, and news. Prof. Bagci was co-chair of Image Processing Track of SPIE Medical Imaging Conference, 2017.
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My research interests focus on two aspects. First, I have been developing effective and high-throughput scientific methods in the following majors:
Second, I have been using these techniques to address challenging problems in computational radiology and biomedical engineering applications. My expertise include the following keywords:
- Medical Image Processing/Analysis,
- Machine Learning/Artificial Intelligence,
- Imaging Modalities: MRI, PET, CT, PET/CT, fMRI, PET/MRI, Histology,
- Computer Vision and Image Processing: Image Segmentation, Image Registration, Shape Analysis, Object Tracking, Image Quantification, Image Enhancement, Object Tracking, Object Recognition, Multi-Organ recognition and segmentation, joint segmentation of multiple images, Gabor wavelets,
- Pattern Recognition: Deep learning, Multi-task learning, Graph Sparsification, Support vector machines, neural networks, Computer aided diagnosis methods, feature extraction, boosting, probabilistic boosting tree, random forest, graph cut, random walk, graph search, probabilistic graphical models, radiomics, feature extraction and dimension reduction, linear discriminant analysis, principal component analysis,
- Clinical and pre-clinical applications: Cardiac Imaging, Abdominal Imaging, Obesity and Metabolic Research, Pulmonary Imaging, Infectious lung disease, lung cancer, pancreas cancer, kidney cancer, liver cancer, nuclear medicine imaging, radiology, fibrosis, breast cancer, pre-clinical imaging, fat quantification, brown fat identification, prostate cancer imaging and analysis.
| Mailing address:
Dr. Ulas Bagci
Center for Research in Computer Vision (CRCV)
4328 Scorpius Street, HEC 221, UCF
Orlando, Florida 32816, USA.
Last updated September, 2015 by Ulas Bagci.