Скачать или смотреть Webinar #11: Explore Correlated Image-Text features - Prof. Luping Zhou

Title: Explore Correlated Image-Text features for Automated Rediographical Report Generation

Abstract:
Automated radiographical report generation is a challenging task as it requires to generate paragraphs describing fine-grained visual differences of cases, especially for those between the diseased and the healthy. Existing image captioning methods commonly target at generic images, and they lack mechanisms to deal with this challenge. As a result, they tend to generate rigid reports that repeat frequently appearing phrases describing the common content of images while suppressing the less frequent but more informative disease-related words. In this talk, I will introduce our efforts at exploring fine-grained interactions of image and text features for radiographically report generation, and demonstrate the success on large benchmarks.

Bio:
Dr. Luping Zhou is an Associate Professor in School of Electrical and Information Engineering, The University of Sydney. She obtained her PhD from Australian National University and got her post-doctoral training in University of North Carolina at Chapel Hill. Dr. Zhou works on the interface of medical image analysis, machine learning, and computer vision, and has published 100+ research papers in these fields. Her current research is focused on medical image analysis with statistical graphical models and deep learning, as well as general visual recognition problems. She was a recipient of the prestigious ARC (Australian Research Council) DECRA award (Discovery Early Career Researcher Award). Dr. Zhou is the Associate Editor of the journals IEEE Trans. on Medical Imaging (TMI) and Pattern Recognition. She is a Senior Member of IEEE.