104 citations · 318 across the 24 of their papers we have counts for
24 papers
DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis
Tiange Xiang, Yang Song, Chaoyi Zhang +6
We present a novel weakly-supervised framework for classifying whole slide images (WSIs). WSIs, due to their gigapixel resolution, are commonly processed by patch-wise classificati…
PSGR: Pixel-wise Sparse Graph Reasoning for COVID-19 Pneumonia Segmentation in CT Images
Haozhe Jia, Haoteng Tang, Guixiang Ma +4
Automated and accurate segmentation of the infected regions in computed tomography (CT) images is critical for the prediction of the pathological stage and treatment response of CO…
Boundary-aware Graph Reasoning for Semantic Segmentation
Haoteng Tang, Haozhe Jia, Weidong Cai +3
In this paper, we propose a Boundary-aware Graph Reasoning (BGR) module to learn long-range contextual features for semantic segmentation. Rather than directly construct the graph…
BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation
Xinyi Wang, Tiange Xiang, Chaoyi Zhang +4
The recurrent mechanism has recently been introduced into U-Net in various medical image segmentation tasks. Existing studies have focused on promoting network recursion via reusin…
H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task
Haozhe Jia, Weidong Cai, Heng Huang +1
In this paper, we propose a Hybrid High-resolution and Non-local Feature Network (H2NF-Net) to segment brain tumor in multimodal MR images. Our H2NF-Net uses the single and cascade…
Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data
Bin Gu, Zhiyuan Dang, Xiang Li +1
In a lot of real-world data mining and machine learning applications, data are provided by multiple providers and each maintains private records of different feature sets about com…
BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture
Tiange Xiang, Chaoyi Zhang, Dongnan Liu +3
U-Net has become one of the state-of-the-art deep learning-based approaches for modern computer vision tasks such as semantic segmentation, super resolution, image denoising, and i…
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting
Dongnan Liu, Donghao Zhang, Yang Song +5
Unsupervised domain adaptation (UDA) for nuclei instance segmentation is important for digital pathology, as it alleviates the burden of labor-intensive annotation and domain shift…
Panoptic Feature Fusion Net: A Novel Instance Segmentation Paradigm for Biomedical and Biological Images
Dongnan Liu, Donghao Zhang, Yang Song +2
Instance segmentation is an important task for biomedical and biological image analysis. Due to the complicated background components, the high variability of object appearances, n…
Region and Object based Panoptic Image Synthesis through Conditional GANs
Heng Wang, Donghao Zhang, Yang Song +3
Image-to-image translation is significant to many computer vision and machine learning tasks such as image synthesis and video synthesis. It has primary applications in the graphic…
Robust Linear Discriminant Analysis Using Ratio Minimization of L1,2-Norms
Feiping Nie, Hua Wang, Zheng Wang +1
As one of the most popular linear subspace learning methods, the Linear Discriminant Analysis (LDA) method has been widely studied in machine learning community and applied to many…
3D Global Convolutional Adversarial Network\\ for Prostate MR Volume Segmentation
Haozhe Jia, Yang Song, Donghao Zhang +5
Advanced deep learning methods have been developed to conduct prostate MR volume segmentation in either a 2D or 3D fully convolutional manner. However, 2D methods tend to have limi…
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization
Kamran Ghasedi Dizaji, Amirhossein Herandi, Cheng Deng +2
Image clustering is one of the most important computer vision applications, which has been extensively studied in literature. However, current clustering methods mostly suffer from…
A Harmonic Mean Linear Discriminant Analysis for Robust Image Classification
Shuai Zheng, Feiping Nie, Chris Ding +1
Linear Discriminant Analysis (LDA) is a widely-used supervised dimensionality reduction method in computer vision and pattern recognition. In null space based LDA (NLDA), a well-kn…
A Closed Form Solution to Multi-View Low-Rank Regression
Shuai Zheng, Xiao Cai, Chris Ding +2
Real life data often includes information from different channels. For example, in computer vision, we can describe an image using different image features, such as pixel intensity…
Enhancing Sentence Relation Modeling with Auxiliary Character-level Embedding
Peng Li, Heng Huang
Neural network based approaches for sentence relation modeling automatically generate hidden matching features from raw sentence pairs. However, the quality of matching feature rep…
Clinical Information Extraction via Convolutional Neural Network
Peng Li, Heng Huang
We report an implementation of a clinical information extraction tool that leverages deep neural network to annotate event spans and their attributes from raw clinical notes and pa…
Non-Greedy L21-Norm Maximization for Principal Component Analysis
Feiping Nie, Heng Huang
Principal Component Analysis (PCA) is one of the most important unsupervised methods to handle high-dimensional data. However, due to the high computational complexity of its eigen…
Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning
Wenhao Jiang, Cheng Deng, Wei Liu +3
Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically…
A Convex Sparse PCA for Feature Analysis
Xiaojun Chang, Feiping Nie, Yi Yang +1
Principal component analysis (PCA) has been widely applied to dimensionality reduction and data pre-processing for different applications in engineering, biology and social science…
Improved Spectral Clustering via Embedded Label Propagation
Xiaojun Chang, Feiping Nie, Yi Yang +1
Spectral clustering is a key research topic in the field of machine learning and data mining. Most of the existing spectral clustering algorithms are built upon Gaussian Laplacian…
Coarse-to-Fine Classification via Parametric and Nonparametric Models for Computer-Aided Diagnosis
Meizhu Liu, Le Lu, Xiaojing Ye +1
Classification is one of the core problems in Computer-Aided Diagnosis (CAD), targeting for early cancer detection using 3D medical imaging interpretation. High detection sensitivi…
An Iterative Locally Linear Embedding Algorithm
Deguang Kong, Chris H. Q. Ding, Heng Huang +1
Local Linear embedding (LLE) is a popular dimension reduction method. In this paper, we first show LLE with nonnegative constraint is equivalent to the widely used Laplacian embedd…
Are Tensor Decomposition Solutions Unique? On the global convergence of HOSVD and ParaFac algorithms
Dijun Luo, Heng Huang, Chris Ding
For tensor decompositions such as HOSVD and ParaFac, the objective functions are nonconvex. This implies, theoretically, there exists a large number of local optimas: starting from…