Speaker: Hamid Tizhoosh, Professor
KIMIA Lab (Laboratory for Knowledge Inference in Medical Image Analysis)
Faculty of Engineering, University of Waterloo
Large archives of digital scans in pathology are slowly becoming a reality. The amount of information in such archives is not only overwhelming but also, not easily accessible. Fast and reliable search engines, customized for histopathology to perform content-based image retrieval, are urgently needed for a more efficient and informed decision making.
While the mainstream AI is working on classification-oriented framework to make decisions, on behalf of medical/clinical experts, the retrieval approach, in contrast, does not seek to replace the human expert but rather offer assistance by tapping into the collective wisdom of evidently diagnosed cases from the past. Through an ensemble approach, KIMIA Lab at University of Waterloo, offers search engine prototype that exploits the strengths of both handcrafted and deep features for image characterization. The idea of contentbased”barcodes” is subsequently used to accelerate the retrieval process.
Date: Tuesday, July 31, 2018
Time: 3:00 – 4:00 pm
Location: BR 5-20/21, OICR, West Tower, MaRS
Event Organizers:Vanya Peltekova, PhD., Lead, BioLab, OICR