BIDS hosted the third annual ImageXD Workshop on May 16–18. ImageXD (Image Processing Across Domains) bringing together researchers from a variety of fields, who share an interest in applications of, as well as algorithms and software for, image analysis.
Date/Time: May 16-18, 2018, 9:00 AM - 5:00 PM
Location: Berkeley Institute for Data Science, 190 Doe Library, UC Berkeley
Registration is now closed; this event has reached full capacity.
Program - This three-day event featured tutorials, talks, and collaborative work sessions:
- Tutorials on image processing software tools such as NumPy, SciPy, scikit-image, Keras, TensorFlow, and Dask.
- Presentations and lunchtime panels about algorithms & solutions utilized by practitioners in fields other than your own.
- Collaborative work sessions with time to build software, explore new methods, develop educational material, or solve an existing research problem with a technique from another domain.
Maxim Ziatdinov — Oak Ridge National Laboratory
Deep learning for atomically resolved imaging techniques: chemical identification and tracking local transformations
James Coughlan — Smith-Kettlewell Eye Research Institute
Computer vision for the visually impaired
Amit Kapadia — Planet Labs
Building Global Mosaics
Natalie Larson — UC Santa Barbara
In-situ X-ray computed tomography for defect evolution
John Canny — UC Berkeley
Deep net visualization, interpretable driving
John Kirkham — Howard Hughes Medical Institute
Interactively analyzing larger than memory neural imaging data
Matt McCormick — Kitware, Inc.
Interactive Analysis and Visualization of Large Images in the Web Browser
Suhas Somnath — Oak Ridge National Laboratory
Pycroscopy - a python package for analyzing, storing, and visualizing multidimensional scientific imaging data
James Sethian — CAMERA, Lawrence Berkeley National Laboratory
Mathematics for image across domains
Deep Ganguli — Chan Zuckerberg Initiative
Starfish: A Python library for Image Based Transcriptomics
Daniela Ushizima, BIDS, UC Berkeley, LBNL
Stéfan van der Walt, BIDS, UC Berkeley, UCSF
Maryam Vareth, BIDS, UC Berkeley, UCSF
Dmitriy Morozov, BIDS, UC Berkeley, LBNL
Maryana Alegro, BIDS, UC Berkeley, UCSF
Elizabeth Brashers, BIDS, UC Berkeley
For more information:
2018 ImageXD Workshop– using images to cross science boundaries and domains
May 22, 2018 | Daniela Ushizima | BIDS Blog: Data Science Insights
BIDS Faculty Affiliate Dani Ushizima is a Staff Scientist in the Computational Research Division at LBNL, where she leads the Image Processing/Machine Vision team at CAMERA. She also leads the Center for Recognition and Inspection of Cells (CRIC), where her research focuses on imaging cancer cells for early-stage disease diagnosis. With 20 years of research and development experience in Computer Vision, Dani has focused primarily on quantitative microscopy and microstructure classification, from materials science to biomedical imaging.
Stéfan van der Walt is a researcher at BIDS, where he leads the Software Working Group. He is the founder of scikit-image and co-author of Elegant SciPy. Stéfan has been developing scientific open source software for more than a decade, focusing mainly on Python packages such as NumPy & SciPy. Outside work, he enjoys traveling, running, and the great outdoors.
Maryam Vareth leads BIDS’ data science research efforts in the Health & Life Sciences. Dr. Vareth is a Co-Director of the Innovate For Health initiative, a collaboration among UC Berkeley, UCSF, and Janssen Pharmaceutical Companies of Johnson & Johnson. As an experienced engineer, researcher, and data scientist, she applies mathematics, statistics and physics to solve unmet needs in healthcare to enhance patients’ experience during their medical journey. She is an advocate for “data-driven” medicine, and in particular for linking medical imaging data with medical diagnostics and therapeutics to extract clinically-relevant insights through the use of open research and open source practices. Dr. Vareth received her BS and MS training in Electrical Engineering and Computer Science (EECS) from UC Berkeley, where she was awarded the prestigious Regent’s and Chancellor’s Scholarship. She completed her PhD through the joint UC Berkeley-UCSF Bioengineering program as a National Science Foundation Fellow, where she was awarded the Margaret Hart Surbeck Endowed Fellowship for Interdisciplinary Research for her work on developing new techniques and algorithms for the acquisition, reconstruction and quantitative analysis of Magnetic Resonance Spectroscopy Imaging (MRSI), with the goal of improving its speed, sensitivity and specificity to improve the management of patients with brain tumors. She conducted her post-doctoral fellowship at UCSF, combining structural, physiological and metabolic imaging data from large clinical trials to quantitatively characterize heterogeneity within malignant brain tumors.
Dmitriy Morozov is a research scientist in the Computational Research Division of the Lawrence Berkeley National Laboratory (LBNL). After completing his PhD in computer science at Duke University, he was a postdoctoral scholar in the Departments of Computer Science and Mathematics at Stanford University and later LBNL. Dmitriy’s work is concerned with geometric and topological data analysis, especially with the development of efficient algorithms and software in this field.
Computer Scientist Maryana Alegro is a former BIDS-BCHIS Data Science Fellow, now an Associate Professional Researcher at UCSF. At UC Berkeley, she was a post-doc at the UCSF Grinberg Lab, where she investigated and created computational tools to assist researchers in studying human brain and dementia, especially Alzheimer’s disease (AD). Such tools incorporate a combination of machine learning techniques with visualization and computer vision. She was also responsible for the design/construction of prototype imaging equipment at the lab. She received her MS and PhD in electrical engineering from the University of São Paulo Polytechnic School. Her major experience is in medical imaging, especially in MRI and histological image analysis.