32 citations · 78 across the 11 of their papers we have counts for
11 papers
Learning to Segment from Noisy Annotations: A Spatial Correction Approach
Jiachen Yao, Yikai Zhang, Songzhu Zheng +3
Noisy labels can significantly affect the performance of deep neural networks (DNNs). In medical image segmentation tasks, annotations are error-prone due to the high demand in ann…
Topology-Aware Uncertainty for Image Segmentation
Saumya Gupta, Yikai Zhang, Xiaoling Hu +2
Segmentation of curvilinear structures such as vasculature and road networks is challenging due to relatively weak signals and complex geometry/topology. To facilitate and accelera…
On the Convergence of Optimizing Persistent-Homology-Based Losses
Yikai Zhang, Jiachen Yao, Yusu Wang +1
Topological loss based on persistent homology has shown promise in various applications. A topological loss enforces the model to achieve certain desired topological property. Desp…
A Manifold View of Adversarial Risk
Wenjia Zhang, Yikai Zhang, Xiaoling Hu +3
The adversarial risk of a machine learning model has been widely studied. Most previous works assume that the data lies in the whole ambient space. We propose to take a new angle a…
Topological Detection of Trojaned Neural Networks
Songzhu Zheng, Yikai Zhang, Hubert Wagner +2
Deep neural networks are known to have security issues. One particular threat is the Trojan attack. It occurs when the attackers stealthily manipulate the model's behavior through…
Learning with Feature-Dependent Label Noise: A Progressive Approach
Yikai Zhang, Songzhu Zheng, Pengxiang Wu +2
Label noise is frequently observed in real-world large-scale datasets. The noise is introduced due to a variety of reasons; it is heterogeneous and feature-dependent. Most existing…
Stability of SGD: Tightness Analysis and Improved Bounds
Yikai Zhang, Wenjia Zhang, Sammy Bald +3
Stochastic Gradient Descent (SGD) based methods have been widely used for training large-scale machine learning models that also generalize well in practice. Several explanations h…
Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach
Yikai Zhang, Hui Qu, Qi Chang +3
Recently, Generative Adversarial Networks (GANs) have demonstrated their potential in federated learning, i.e., learning a centralized model from data privately hosted by multiple…
Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information
Qi Chang, Zhennan Yan, Lohendran Baskaran +5
As deep learning technologies advance, increasingly more data is necessary to generate general and robust models for various tasks. In the medical domain, however, large-scale and…
Learn distributed GAN with Temporary Discriminators
Hui Qu, Yikai Zhang, Qi Chang +3
In this work, we propose a method for training distributed GAN with sequential temporary discriminators. Our proposed method tackles the challenge of training GAN in the federated…
Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data
Qi Chang, Hui Qu, Yikai Zhang +4
In this paper, we propose a data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN). Our…