Visual Recognition for Medical Images

In conjunction with ICCV 2019

During last few years, visual recognition based on deep learning is receiving more attention in the medical image domain, where there is still much room for compensating human ability with machine vision. This workshop is dedicated to addressing the current challenges of visual recognition model development in medical image domain. By bringing leading researchers together and let them present, discuss, and share their up-to-date research outcomes, we expect this workshop contributes to solving fundamental research problems both in the field of visual recognition and medicine.

Program

Date & Time

09:00-17:00, Sunday, October 27.

Location

Room 327 B-C, COEX Convention Center.

Program Outline

Scroll through the time table to see all the contents. For more information of papers, please refer to the accepted papers.

Invited Speakers

Martin Stumpe (Tempus)

Martin Stumpe works on advancing precision medicine and cancer care using deep learning. He is currently SVP for Data Science at Tempus. Before joining Tempus, he founded and led the Pathology project at the pathology team in Google Brain, which is focused on improving the accuracy and availability of cancer diagnostics. Prior to that, he worked at NASA Ames Research Center and SETI in Mountain View to develop signal processing algorithms for detecting extrasolar planets. Martin has a PhD in Computational and Theoretical Physics from the Max Planck Institute for Biophysical Chemistry in Goettingen, Germany, and did a postdoc at Stanford University on protein folding dynamics.

Dinggang Shen (UNC-Chapel Hill)

Dinggang Shen is Jeffrey Houpt Distinguished Investigator, and a Professor in the Department of Radiology and BRIC at UNC-Chapel Hill. His research interests include medical image analysis, computer vision, and pattern recognition. He has published more than 1,000 papers in the international journals and conference proceedings. He serves as an editorial board member for eight international journals. He has also served in the Board of Directors of MICCAI Society, in 2012-2015, and will be General Chair for MICCAI 2019. He is Fellow of IEEE, Fellow of The American Institute for Medical and Biological Engineering, and also Fellow of The International Association for Pattern Recognition.

Anuroop Sriram (Facebook AI Research)

Anuroop is a Research Engineer and Manager at Facebook AI Research (FAIR), working on applying deep learning to medical image reconstruction and speech recognition. At Facebook, he has worked on the fastMRI project, helping with the release of the largest publicly available MRI dataset. Previously, he worked on the Deep Speech 2 project at Baidu Research, and on building machine learning models for computational advertising at Twitter. Anuroop has a Master of Science degree in Language Technologies from Carnegie Mellon University.

Hyeonseob Nam is a research scientist at Lunit, working on deep learning for medical images. He received his B.Sc. and M.Sc. degrees in Computer Science and Engineering from Pohang University of Science and Technology, where he pioneered the intersection of deep learning and visual tracking. Before joining Lunit in 2017, he worked as a research engineer at Naver Labs to develop applications of computer vision and machine learning. His current research focus is robustness of models under a variety of real-world scenarios.

Sanja Fidler (University of Toronto and NVIDIA)

Sanja Fidler is an Assistant Professor at University of Toronto, and a Director of AI at NVIDIA, leading a research lab in Toronto. Prior coming to Toronto, in 2012/2013, she was a Research Assistant Professor at Toyota Technological Institute at Chicago, an academic institute located in the campus of University of Chicago. She did her postdoc with Prof. Sven Dickinson at University of Toronto in 2011/2012. She finished her PhD in 2010 at University of Ljubljana in Slovenia in the group of Prof. Ales Leonardis. In 2010, she was visiting Prof. Trevor Darrell's group at UC Berkeley and ICSI. She got her BSc degree in Applied Math at University of Ljubljana.

Organizing Committee

Senior research scientist and solutions architect at NVIDIA

Assistant professor at NYU and research scientist at FAIR

Co-founder and VP of Research at Lunit

Program Committee

We thank all the program committee members for their participation and supports. Also, we would like to thank all the applicants who are not listed here.

- Adriana Romero (Facebook AI Research, United States)

- Amy Zhao (MIT, United States)

- Bingzhe Wu (Peking University, China)

- Dwarikanath Mahapatra (IBM Research, Australia)

- Guha Balakrishnan (MIT, United States)

- Huan Qi (University of Oxford, United Kingdom)

- Jaehwan Lee (Lunit, South Korea)

- Jongchan Park (Lunit, South Korea)

- Juan Caicedo (Broad Institute, United States)

- Kuan-Lun Tseng (National Taiwan University, Taiwan)

- Kyunghyun Sung (UCLA, United States)

- Lequan Yu (The Chinese University of Hong Kong, Hong Kong)

- Lingxi Xie (Huawei Noah's Ark Lab, Huawei Inc.)

- Mahdi Hosseini (University of Toronto, Canada)

- Miguel Angel González Ballester (Pompeu Fabra University, Spain)

- Saeed Izadi (Simon Fraser University, Canada)

- Sérgio Pereira (University of Minho, Portugal)

- Subhashini Venugopalan (Google, United States)

- Xin Yang (The Chinese University of Hong Kong, Hong Kong)

- Yifan Peng (NIH, United States)

- Zhiming Cui (University of Hong Kong, Hong Kong)

- Zongwei Zhou (Arizona State University, United States)