Past Events

PhD Defense

Generative Model and Latent Space Based Medical Image Analysis and Applications


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Monday, September 19, 2022, 10:00am


Speaker: Qi Chang

Location : Virtual


Professor Dimitris N. Metaxas (Chair) Professor Konstantinos P. Michmizos Professor Karl Stratos Professor Xiaolei Huang(Penn State University)

Event Type: PhD Defense

Abstract: The generative models have gained much attention in the computer vision community in many cases, such as domain adaptation, data augmentation, and image-to-image translation. These properties have been rapidly applied in some medical domains, but the generative model's potential is not fully explored. This dissertation studies new perspectives on the distributed generative models and latent space manipulation to address some of the most critical medical tasks: medical image private data sharing and cardiac motion analysis. First, we work on asynchronized distributed GAN(AsynDGAN) paradigm to learn the distribution across several private medical data centers and adopt the well-trained generator as a medical data provider for the future use of the downstream tasks. Further, I work on some real scenarios under the continuous learning(Life-long learning) settings with the distributed GAN with temporary discriminators(TDGAN). Such a method could prevent the model from catastrophic forgetting when continuously learning new incoming data. The multi-modality and missing-modality settings are also systematically analyzed. By using a multi-modality adaptive learning model and network(Modality Bank), the Modality Bank could auto-complete the missing modalities and generate multiple modality images simultaneously. We demonstrate that the AsynDGAN-related techniques could secure medical privacy while fully using these private data for machine learning applications. Secondly, the dissertation presents a framework for joint 2D cardiac segmentation and 3D volume reconstruction via a structure-specific generative method(DeepRecon). I will present the end-to-end latent-space-based framework that generates multiple clinically essential outcomes, including accurate image segmentation, synthetic high-resolution 3D image, and 3D reconstructed volume. Experimental results demonstrate the effectiveness of our approach on numerous fronts, including 2D segmentation, 3D reconstruction, and downstream 4D motion pattern adaption performance. And the motion adaptation method provides a unique tool to help cardiologists analyze cardiac motion functional differences between various cases. Overall, the approaches demonstrate the importance of the generative models for the newly emerging medical analysis domains for 3D reconstruction, motion analysis, and privacy data sharing.


Department of Computer Science

School of Arts & Sciences

Rutgers University

Contact  Professor Dimitris Metaxas