20180829-27 杨欣：Deep Neural Networks for Automated Prostate Cancer Detection a ...

报告嘉宾：杨欣（华中科技大学）

报告时间：2018年08月29日（星期三）晚上20:00（北京时间）

报告题目：Deep Neural Networks for Automated Prostate Cancer Detection and Diagnosis in Multi-parametric MRI

主持人：王兴刚（华中科技大学）

报告人简介：

Xin Yang received her PhD degree in University of California, Santa Barbara in 2013. She worked as a Post-doc in Learning-based Multimedia Lab at UCSB (2013-2014). She joined Huazhong University of Science and Technology in August 2014 and is currently the Associate Professor of School of Electronic Information and Communications. Her research interests include medical image analysis, monocular simultaneous localization and mapping, and augmented reality. She has published over 40 technical papers, including TPAMI, TMI, MeDIA, TMM, TVCG, ACM MM, MICCAI, ECCV, etc., co-authored two books and held 10+ U.S. and Chinese Patents and software copyrights. Prof. Yang is a member of IEEE and a member of ACM.

个人主页：

https://sites.google.com/view/xinyang/home

报告摘要：

Multi-parameter magnetic resonance imaging (mp-MRI) is increasingly popular for prostate cancer (PCa) detection and diagnosis. However, interpreting mp-MRI data which typically contains multiple unregistered 3D sequences, e.g. apparent diffusion coefficient (ADC) maps and T2-weighted (T2w) images, is time-consuming and demands special expertise, limiting its usage for large-scale PCa screening. Therefore, solutions to computer-aided detection and diagnosis of PCa in mp-MRI images are highly desirable. In this talk I will introduce a series of our recent works on utilizing deep convolutional neural networks (CNN) for automated PCa detection and diagnosis. I will introduce our co-trained weakly-supervised CNNs which can concurrently identify the presence of PCa in an image and localize lesions. Our weakly-supervised CNNs are trained with entire prostate images with only image-level labels indicating the presence or absence of PCa, significantly alleviating the manual annotation efforts in clinical usage. I will also introduce our Tissue Deformation Network (TDN) for automated prostate detection and multimodal registration. The TDN can be directly integrated any PCa detection CNNs so that all parameters of the entire network can be jointly optimized in an end-to-end manner. In addition, I will describe our recent method for mp-MRI image synthesis based on generative adversarial learning.

参考文献：

[1] Yang X., Liu C. Y., Wang Z. W.*, Yang J., Le M. H., Wang L., Cheng K. –T. Co-trained Convolutional Neural Networks for Automated Detection of Prostate Cancer in Multi-parametric MRI，Medical Image Analysis, (MeDIA) 2017

[2] Wang Z. W., Liu C. Y., Wang L., Yang X.*, Cheng K.-T. Automated Detection of Clinically Significant Prostate Cancer in mp-MRI Images based on an End-to-End Deep Neural Network，IEEE Trans. on Medical Imaging (TMI) 2017

[3] Wang Z.W., Lin Y., Cheng K.-T., Yang X.*, StitchAD-GAN for Synthesizing Apparent Diffusion Coefficient Images of Clinically Significant Prostate Cancer, British Machine Vision Conference (BMVC), 2018

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特别鸣谢本次Webinar主要组织者：

VOOC责任委员：王兴刚（华中科技大学）

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