Workshop on Medical Computer Vision at CVPR 2015, Boston, MA (http://www.pamitc.org/cvpr15/)
Date: post-conference, June 11 2015
Registration: via IEEE CVPR
This workshop aims at exploring the use of modern computer vision and deep learning (D-CNN) technology to scale up the medical imaging parsing and
understanding problems. In the past, the state-of-the-art work were studying sub-thousand patients to be considered as "big-data" in the medical imaging domain.
We will use this workshop as a platform to discuss relevant issues in releasing the power of hospital-scale medical image database (e.g., hundreds of thousands
of patients from a modern hospital's PACS database). We strongly believe that indeed "big data" + "deep learning" would make big difference in the next few years.
Our goal is simply to discuss how to reproduce the success of ILSVRC (http://www.image-net.org/challenges/LSVRC/2014/ ) in truly large-scale medical image
analysis and understanding: what are missing and how to move forward!
Dates
Workshop - June 11 2012 (8:00 am - 12:30 pm)
All invited talks (final program):
[8:00-8:20] Dr. Leo Grady ("Personalized Blood Flow Simulation: Changing the Treatment Paradigm for Cardiovascular Disease", Heartflow, Inc., USA)
[8:20-8:40] Dr. Christophe Chefd'Hotel ("Computational Histopathology - Unlocking Tissue Content in Precision Medicine", Roche Tissue Diagnostics, USA)
[8:40-9:10] Dr. Ronald M. Summers ("Towards per-voxel large-scale radiology image parsing and understanding", NIH, USA)
[9:10-9:40] Prof. Pascal Fua ("Using Machine Learning Techniques to Reconstruct Complex Curvilinear Structures", EPFL, Switzerland)
[9:40-10:00] Prof. Tal Arbel ("Hierarchical Probabilistic Graphical Models for the Detection and Segmentation of Multiple Sclerosis Lesions in Multicentre
Clinical Trial Datasets", McGill University, Canada)
[10:00-10:30] Prof. William (Sandy) Wells, (“Invariant Feature-Based Analysis of Medical Images”, Harvard Medical School, USA)
[10:30-10:50] Prof. Rene Vidal ("Surgical gesture segmentation and recognition from kinematic and video data", JHU, USA)
[10:50-11:10] Prof. Hayit Greenspan ("Large Scale Chest Radiograph Categorization with Deep CNN and No Medical Training", Tel-Aviv University, Israel)
[11:10-11:30] Prof. Petia Radeva ("Using deep learning to detect small intestine disorders", Universitat de Barcelona, Spain)
[11:30-11:50] Prof. Ramesh Raskar, (“Retinal Predictive Health Analytics”, MIT, USA)
[11:50-12:10] Dr. Hoo-chang Shin ("Deep mining in text/image on a hospital scale PACS database: early findings", NIH, USA)
[12:10-12:30] Dr. Tammy Riklin Raviv ("Big data - small training set: Addressing Medical Image Analysis Bottlenecks", MIT, USA)
People
Le Lu (National Institutes of Health Clinical Center, Bethesda, MD, USA)
Yefeng Zheng (Siemens Corporate Technology, Princeton, NJ, USA)
Bjoern Menze (Technical University of Munchen, Germany)
Georg Langs (Medical University of Vienna & MIT, Austria)
Leo Grady (HeartFlow, Inc. Redwood City, CA, USA)