Search Results for author: Xiaodong Wu

Found 20 papers, 1 papers with code

Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic Choices

no code implementations27 Mar 2022 Dixian Zhu, Xiaodong Wu, Tianbao Yang

(i) We benchmark a variety of loss functions with different algorithmic choices for deep AUROC optimization problem.

imbalanced classification

When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee

no code implementations1 Mar 2022 Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, Tianbao Yang

In this paper, we propose systematic and efficient gradient-based methods for both one-way and two-way partial AUC (pAUC) maximization that are applicable to deep learning.

Joint Calibrationless Reconstruction and Segmentation of Parallel MRI

no code implementations19 May 2021 Aniket Pramanik, Xiaodong Wu, Mathews Jacob

We introduce a novel image domain deep-learning framework for calibrationless parallel MRI reconstruction, coupled with a segmentation network to improve image quality and to reduce the vulnerability of current segmentation algorithms to image artifacts resulting from acceleration.

MRI Reconstruction

Learning Similarity between Movie Characters and Its Potential Implications on Understanding Human Experiences

no code implementations NAACL (NUSE) 2021 Zhilin Wang, Weizhe Lin, Xiaodong Wu

While many different aspects of human experiences have been studied by the NLP community, none has captured its full richness.

Optimal Pooling Matrix Design for Group Testing with Dilution (Row Degree) Constraints

no code implementations5 Aug 2020 Jirong Yi, Myung Cho, Xiaodong Wu, Raghu Mudumbai, Weiyu Xu

In this paper, we consider the problem of designing optimal pooling matrix for group testing (for example, for COVID-19 virus testing) with the constraint that no more than $r>0$ samples can be pooled together, which we call "dilution constraint".

Globally Optimal Surface Segmentation using Deep Learning with Learnable Smoothness Priors

no code implementations2 Jul 2020 Leixin Zhou, Xiaodong Wu

Automated surface segmentation is important and challenging in many medical image analysis applications.

Semantic Segmentation

Unsupervised anomaly localization using VAE and beta-VAE

no code implementations19 May 2020 Leixin Zhou, Wenxiang Deng, Xiaodong Wu

An VAE trained on normal images is expected to only be able to reconstruct normal images, allowing the localization of anomalous pixels in an image via manipulating information within the VAE ELBO loss.

Do Deep Minds Think Alike? Selective Adversarial Attacks for Fine-Grained Manipulation of Multiple Deep Neural Networks

no code implementations26 Mar 2020 Zain Khan, Jirong Yi, Raghu Mudumbai, Xiaodong Wu, Weiyu Xu

Recent works have demonstrated the existence of {\it adversarial examples} targeting a single machine learning system.

No, you're not alone: A better way to find people with similar experiences on Reddit

no code implementations WS 2019 Zhilin Wang, Elena Rastorgueva, Weizhe Lin, Xiaodong Wu

This model is built upon the BERT Next Sentence Prediction model and reduces the time complexity for clustering all posts in a corpus from O(n{\^{}}2) to O(n) with respect to the number of posts.

Deep Neural Networks for Surface Segmentation Meet Conditional Random Fields

no code implementations11 Jun 2019 Leixin Zhou, Zisha Zhong, Abhay Shah, Bensheng Qiu, John Buatti, Xiaodong Wu

To the best of our knowledge, this is the first study to apply a 3-D neural network with a CRFs model for direct surface segmentation.

Semantic Segmentation

Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks

no code implementations25 May 2019 Jirong Yi, Hui Xie, Leixin Zhou, Xiaodong Wu, Weiyu Xu, Raghuraman Mudumbai

In this paper, we present a simple hypothesis about a feature compression property of artificial intelligence (AI) classifiers and present theoretical arguments to show that this hypothesis successfully accounts for the observed fragility of AI classifiers to small adversarial perturbations.

Deep segmentation networks predict survival of non-small cell lung cancer

1 code implementation26 Mar 2019 Stephen Baek, Yusen He, Bryan G. Allen, John M. Buatti, Brian J. Smith, Ling Tong, Zhiyu Sun, Jia Wu, Maximilian Diehn, Billy W. Loo, Kristin A. Plichta, Steven N. Seyedin, Maggie Gannon, Katherine R. Cabel, Yusung Kim, Xiaodong Wu

Here we show that CNN trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value.

Tumor Segmentation

Robust Image Segmentation Quality Assessment

no code implementations MIDL 2019 Leixin Zhou, Wenxiang Deng, Xiaodong Wu

Deep learning based image segmentation methods have achieved great success, even having human-level accuracy in some applications.

Semantic Segmentation

Outlier Detection using Generative Models with Theoretical Performance Guarantees

no code implementations26 Oct 2018 Jirong Yi, Anh Duc Le, Tianming Wang, Xiaodong Wu, Weiyu Xu

In this paper, we propose a generative model neural network approach for reconstructing the ground truth signals under sparse outliers.

Outlier Detection

Optimal Multi-Object Segmentation with Novel Gradient Vector Flow Based Shape Priors

no code implementations22 May 2017 Junjie Bai, Abhay Shah, Xiaodong Wu

Shape priors have been widely utilized in medical image segmentation to improve segmentation accuracy and robustness.

Medical Image Segmentation Semantic Segmentation

Simultaneous Multiple Surface Segmentation Using Deep Learning

no code implementations19 May 2017 Abhay Shah, Michael Abramoff, Xiaodong Wu

The task of automatically segmenting 3-D surfaces representing boundaries of objects is important for quantitative analysis of volumetric images, and plays a vital role in biomedical image analysis.

Optimal Surface Segmentation with Convex Priors in Irregularly Sampled Space

no code implementations9 Nov 2016 Abhay Shah, Michael D. Abramoff, Xiaodong Wu

Optimal surface segmentation is a state-of-the-art method used for segmentation of multiple globally optimal surfaces in volumetric datasets.

Medical Image Segmentation Semantic Segmentation +1

Error-tolerant Scribbles Based Interactive Image Segmentation

no code implementations CVPR 2014 Junjie Bai, Xiaodong Wu

The experimental results show that the proposed algorithm is robust to the errors in the user input and preserves the "anchoring" capability of the user input.

Interactive Segmentation Semantic Segmentation

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