Text

theses

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 function 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). The proposed 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.

http://dissertations.umi.com/gsnb.rutgers:12166

Ph.D.

Includes bibliographical references

doi:10.7282/t3-cezf-4z43

The author owns the copyright to this work.