arXiv subjects

Zichen Wang

Publications and source records attributed to Zichen Wang.

76 records · Page 5Linked to original sources

arXiv2022

Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications

Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales. The development of graph convolutional networks (GCNs) has created the opportunity to address this information complexity via graph-driven architectures, since GCNs can perform feature aggregation, interaction, and reasoning with remarkable flexibility and efficiency. These GCNs capabilities have spawned a new wave of research in medical imaging analysis with the overarching goal of improving quantitative disease understanding, monitoring, and diagnosis. Yet daunting challenges remain for designing the important image-to-graph transformation for multi-modality medical imaging and gaining insights into model interpretation and enhanced clinical decision support. In this review, we present recent GCNs developments in the context of medical image analysis including imaging data from radiology and histopathology. We discuss the fast-growing use of graph network architectures in medical image analysis to improve disease diagnosis and patient outcomes in clinical practice. To foster cross-disciplinary research, we present GCNs technical advancements, emerging medical applications, identify common challenges in the use of image-based GCNs and their extensions in model interpretation, large-scale benchmarks that promise to transform the scope of medical image studies and related graph-driven medical research.

eess.IV↗

arXiv2021

On 2-digit and 3-digit Kaprekar's Routine

Kaprekar's Routine is an iteration process that, with each iteration, sorts the digits of a number in descending and ascending orders and uses their difference for the next iteration. In this paper, we successfully address the structure of the fixed periodic sets and the maximum step of iteration to enter a fixed set in the three-digit case and two-digit case, with a focus on the latter. With three-digit, there exists a unique fixed set of the cardinality of 1 or 2, depending on the parity of m, and the maximum step is determined by m. With two-digit, we offer some powerful properties of the fixed sets and a mechanical method to calculate the fixed sets. We also prove that the number of 2-factors in m+1 determines the maximum step (but has completely no influence on the structure of the fixed sets). This continues Atsushi Yamagami's previous studies in 2018 (published in Journal of Number Theory) and completes it with a general solution.

math.NT↗

arXiv2019

Semi-supervised Learning using Adversarial Training with Good and Bad Samples

In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training. Previous results have illustrated that generative adversarial networks (GANs) can be used for multiple purposes. Triple-GAN, which aims to jointly optimize model components by incorporating three players, generates suitable image-label pairs to compensate for the lack of labeled data in SSL with improved benchmark performance. Conversely, Bad (or complementary) GAN, optimizes generation to produce complementary data-label pairs and force a classifier's decision boundary to lie between data manifolds. Although it generally outperforms Triple-GAN, Bad GAN is highly sensitive to the amount of labeled data used for training. Unifying these two approaches, we present unified-GAN (UGAN), a novel framework that enables a classifier to simultaneously learn from both good and bad samples through adversarial training. We perform extensive experiments on various datasets and demonstrate that UGAN: 1) achieves state-of-the-art performance among other deep generative models, and 2) is robust to variations in the amount of labeled data used for training.

cs.LG↗

arXiv2019

Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach

Recently, semi-supervised learning methods based on generative adversarial networks (GANs) have received much attention. Among them, two distinct approaches have achieved competitive results on a variety of benchmark datasets. Bad GAN learns a classifier with unrealistic samples distributed on the complement of the support of the input data. Conversely, Triple GAN consists of a three-player game that tries to leverage good generated samples to boost classification results. In this paper, we perform a comprehensive comparison of these two approaches on different benchmark datasets. We demonstrate their different properties on image generation, and sensitivity to the amount of labeled data provided. By comprehensively comparing these two methods, we hope to shed light on the future of GAN-based semi-supervised learning.

cs.LG↗