Search Results for author: Dimitris Metaxas

Found 54 papers, 30 papers with code

AE-StyleGAN: Improved Training of Style-Based Auto-Encoders

1 code implementation17 Oct 2021 Ligong Han, Sri Harsha Musunuri, Martin Renqiang Min, Ruijiang Gao, Yu Tian, Dimitris Metaxas

StyleGANs have shown impressive results on data generation and manipulation in recent years, thanks to its disentangled style latent space.

Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis from Lung CT Scans with Multi-Scale Guided Dense Attention

1 code implementation29 Sep 2021 Guotai Wang, Shuwei Zhai, Giovanni Lasio, Baoshe Zhang, Byong Yi, Shifeng Chen, Thomas J. Macvittie, Dimitris Metaxas, Jinghao Zhou, Shaoting Zhang

Computed Tomography (CT) plays an important role in monitoring radiation-induced Pulmonary Fibrosis (PF), where accurate segmentation of the PF lesions is highly desired for diagnosis and treatment follow-up.

Computed Tomography (CT) Lesion Segmentation

Global and Local Interpretation of black-box Machine Learning models to determine prognostic factors from early COVID-19 data

no code implementations10 Sep 2021 Ananya Jana, Carlos D. Minacapelli, Vinod Rustgi, Dimitris Metaxas

We explore one of the most recent techniques called symbolic metamodeling to find the mathematical expression of the machine learning models for COVID-19.

severity prediction

Dual Projection Generative Adversarial Networks for Conditional Image Generation

1 code implementation ICCV 2021 Ligong Han, Martin Renqiang Min, Anastasis Stathopoulos, Yu Tian, Ruijiang Gao, Asim Kadav, Dimitris Metaxas

We then propose an improved cGAN model with Auxiliary Classification that directly aligns the fake and real conditionals $P(\text{class}|\text{image})$ by minimizing their $f$-divergence.

Conditional Image Generation

UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation

1 code implementation2 Jul 2021 Yunhe Gao, Mu Zhou, Dimitris Metaxas

In this study, we present UTNet, a simple yet powerful hybrid Transformer architecture that integrates self-attention into a convolutional neural network for enhancing medical image segmentation.

Medical Image Segmentation

Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training

1 code implementation30 Mar 2021 Yunhe Gao, Zhiqiang Tang, Mu Zhou, Dimitris Metaxas

Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks' generalization performance.

Affine Transformation Data Augmentation +1

Liver Fibrosis and NAS scoring from CT images using self-supervised learning and texture encoding

1 code implementation5 Mar 2021 Ananya Jana, Hui Qu, Carlos D. Minacapelli, Carolyn Catalano, Vinod Rustgi, Dimitris Metaxas

The severity and treatment of NAFLD is determined by NAFLD Activity Scores (NAS)and liver fibrosis stage, which are usually obtained from liver biopsy.

Self-Supervised Learning Transfer Learning

Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach

1 code implementation9 Feb 2021 Yikai Zhang, Hui Qu, Qi Chang, Huidong Liu, Dimitris Metaxas, Chao Chen

A federatedGAN jointly trains a centralized generator and multiple private discriminators hosted at different sites.

Federated Learning

CrossNorm and SelfNorm for Generalization under Distribution Shifts

1 code implementation ICCV 2021 Zhiqiang Tang, Yunhe Gao, Yi Zhu, Zhi Zhang, Mu Li, Dimitris Metaxas

Can we develop new normalization methods to improve generalization robustness under distribution shifts?

Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization

1 code implementation CVPR 2021 Long Zhao, Yuxiao Wang, Jiaping Zhao, Liangzhe Yuan, Jennifer J. Sun, Florian Schroff, Hartwig Adam, Xi Peng, Dimitris Metaxas, Ting Liu

To evaluate the power of the learned representations, in addition to the conventional fully-supervised action recognition settings, we introduce a novel task called single-shot cross-view action recognition.

Action Recognition Contrastive Learning +1

Deep Subspace Clustering with Data Augmentation

no code implementations NeurIPS 2020 Mahdi Abavisani, Alireza Naghizadeh, Dimitris Metaxas, Vishal Patel

In particular, we introduce a temporal ensembling component to the objective function of DSC algorithms to enable the DSC networks to maintain consistent subspaces for random transformations in the input data.

Data Augmentation

Error-Bounded Correction of Noisy Labels

3 code implementations ICML 2020 Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris Metaxas, Chao Chen

To be robust against label noise, many successful methods rely on the noisy classifiers (i. e., models trained on the noisy training data) to determine whether a label is trustworthy.

Image Classification

Deep Learning based NAS Score and Fibrosis Stage Prediction from CT and Pathology Data

1 code implementation22 Sep 2020 Ananya Jana, Hui Qu, Puru Rattan, Carlos D. Minacapelli, Vinod Rustgi, Dimitris Metaxas

In this work, we propose a novel method to automatically predict NAS score and fibrosis stage from CT data that is non-invasive and inexpensive to obtain compared with liver biopsy.

Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks

no code implementations25 Aug 2020 Qiaoying Huang, Eric Z. Chen, Hanchao Yu, Yimo Guo, Terrence Chen, Dimitris Metaxas, Shanhui Sun

We also analyze thickness patterns on different cardiac pathologies with a standard clinical model and the results demonstrate the potential clinical value of our method for thickness based cardiac disease diagnosis.

Enhanced MRI Reconstruction Network using Neural Architecture Search

no code implementations19 Aug 2020 Qiaoying Huang, Dong Yang, Yikun Xian, Pengxiang Wu, Jingru Yi, Hui Qu, Dimitris Metaxas

The accurate reconstruction of under-sampled magnetic resonance imaging (MRI) data using modern deep learning technology, requires significant effort to design the necessary complex neural network architectures.

MRI Reconstruction Neural Architecture Search

PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data

no code implementations18 Aug 2020 Meng Ye, Qiaoying Huang, Dong Yang, Pengxiang Wu, Jingru Yi, Leon Axel, Dimitris Metaxas

The 3D volumetric shape of the heart's left ventricle (LV) myocardium (MYO) wall provides important information for diagnosis of cardiac disease and invasive procedure navigation.

Semantic Segmentation

Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors

1 code implementation17 Aug 2020 Jingru Yi, Pengxiang Wu, Bo Liu, Qiaoying Huang, Hui Qu, Dimitris Metaxas

To address this issue, in this work we extend the horizontal keypoint-based object detector to the oriented object detection task.

Object Detection In Aerial Images Oriented Object Detection

Learn distributed GAN with Temporary Discriminators

1 code implementation ECCV 2020 Hui Qu, Yikai Zhang, Qi Chang, Zhennan Yan, Chao Chen, Dimitris Metaxas

Our proposed method tackles the challenge of training GAN in the federated learning manner: How to update the generator with a flow of temporary discriminators?

Federated Learning

Unbiased Auxiliary Classifier GANs with MINE

1 code implementation13 Jun 2020 Ligong Han, Anastasis Stathopoulos, Tao Xue, Dimitris Metaxas

To remedy this, Twin Auxiliary Classifier GAN (TAC-GAN) introduces a twin classifier to the min-max game.

Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data

1 code implementation CVPR 2020 Qi Chang, Hui Qu, Yikai Zhang, Mert Sabuncu, Chao Chen, Tong Zhang, Dimitris Metaxas

In this paper, we propose a data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN).

MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps

1 code implementation CVPR 2020 Pengxiang Wu, Siheng Chen, Dimitris Metaxas

The backbone of MotionNet is a novel spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion.

3D Object Detection Autonomous Driving +1

Point Cloud Processing via Recurrent Set Encoding

no code implementations25 Nov 2019 Pengxiang Wu, Chao Chen, Jingru Yi, Dimitris Metaxas

The spatial layout of the beams is regular, and this allows the beam features to be further fed into an efficient 2D convolutional neural network (CNN) for hierarchical feature aggregation.

Robust Conditional GAN from Uncertainty-Aware Pairwise Comparisons

1 code implementation21 Nov 2019 Ligong Han, Ruijiang Gao, Mun Kim, Xin Tao, Bo Liu, Dimitris Metaxas

Conditional generative adversarial networks have shown exceptional generation performance over the past few years.

Label Cleaning with Likelihood Ratio Test

no code implementations25 Sep 2019 Songzhu Zheng, Pengxiang Wu, Aman Goswami, Mayank Goswami, Dimitris Metaxas, Chao Chen

To collect large scale annotated data, it is inevitable to introduce label noise, i. e., incorrect class labels.

Unsupervised Domain Adaptation via Calibrating Uncertainties

1 code implementation25 Jul 2019 Ligong Han, Yang Zou, Ruijiang Gao, Lezi Wang, Dimitris Metaxas

Unsupervised domain adaptation (UDA) aims at inferring class labels for unlabeled target domain given a related labeled source dataset.

Unsupervised Domain Adaptation

Effective 3D Humerus and Scapula Extraction using Low-contrast and High-shape-variability MR Data

no code implementations22 Feb 2019 Xiaoxiao He, Chaowei Tan, Yuting Qiao, Virak Tan, Dimitris Metaxas, Kang Li

For the initial shoulder preoperative diagnosis, it is essential to obtain a three-dimensional (3D) bone mask from medical images, e. g., magnetic resonance (MR).

Brain Segmentation from k-space with End-to-end Recurrent Attention Network

no code implementations5 Dec 2018 Qiaoying Huang, Xiao Chen, Dimitris Metaxas, Mariappan S. Nadar

The task of medical image segmentation commonly involves an image reconstruction step to convert acquired raw data to images before any analysis.

Brain Image Segmentation Brain Segmentation +1

MRI Reconstruction via Cascaded Channel-wise Attention Network

1 code implementation18 Oct 2018 Qiaoying Huang, Dong Yang, Pengxiang Wu, Hui Qu, Jingru Yi, Dimitris Metaxas

We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate.

MRI Reconstruction

Quantized Densely Connected U-Nets for Efficient Landmark Localization

1 code implementation ECCV 2018 Zhiqiang Tang, Xi Peng, Shijie Geng, Lingfei Wu, Shaoting Zhang, Dimitris Metaxas

Finally, to reduce the memory consumption and high precision operations both in training and testing, we further quantize weights, inputs, and gradients of our localization network to low bit-width numbers.

Face Alignment Pose Estimation

Learning to Forecast and Refine Residual Motion for Image-to-Video Generation

1 code implementation ECCV 2018 Long Zhao, Xi Peng, Yu Tian, Mubbasir Kapadia, Dimitris Metaxas

We consider the problem of image-to-video translation, where an input image is translated into an output video containing motions of a single object.

Human Pose Forecasting Translation +1

Self-Attention Generative Adversarial Networks

44 code implementations arXiv 2018 Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena

In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks.

Conditional Image Generation

Improving GANs Using Optimal Transport

2 code implementations ICLR 2018 Tim Salimans, Han Zhang, Alec Radford, Dimitris Metaxas

We present Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution.

Image Generation

Toward Marker-free 3D Pose Estimation in Lifting: A Deep Multi-view Solution

no code implementations6 Feb 2018 Rahil Mehrizi, Xi Peng, Zhiqiang Tang, Xu Xu, Dimitris Metaxas, Kang Li

The results are also compared with state-of-the-art methods on HumanEva-I dataset, which demonstrates the superior performance of our approach.

3D Pose Estimation

Interactive Reinforcement Learning for Object Grounding via Self-Talking

no code implementations2 Dec 2017 Yan Zhu, Shaoting Zhang, Dimitris Metaxas

In this paper, we introduce an interactive training method to improve the natural language conversation system for a visual grounding task.

Visual Grounding

StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

16 code implementations19 Oct 2017 Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, Dimitris Metaxas

In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images.

Text-to-Image Generation

Automatic Liver Segmentation Using an Adversarial Image-to-Image Network

no code implementations25 Jul 2017 Dong Yang, Daguang Xu, S. Kevin Zhou, Bogdan Georgescu, Mingqing Chen, Sasa Grbic, Dimitris Metaxas, Dorin Comaniciu

Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment.

Liver Segmentation

Reconstruction-Based Disentanglement for Pose-invariant Face Recognition

no code implementations ICCV 2017 Xi Peng, Xiang Yu, Kihyuk Sohn, Dimitris Metaxas, Manmohan Chandraker

Finally, we propose a new feature reconstruction metric learning to explicitly disentangle identity and pose, by demanding alignment between the feature reconstructions through various combinations of identity and pose features, which is obtained from two images of the same subject.

Face Recognition Metric Learning +1

Detection of Major ASL Sign Types in Continuous Signing For ASL Recognition

no code implementations LREC 2016 Polina Yanovich, Carol Neidle, Dimitris Metaxas

In American Sign Language (ASL) as well as other signed languages, different classes of signs (e. g., lexical signs, fingerspelled signs, and classifier constructions) have different internal structural properties.

Multiple Instance Learning

Mode Estimation for High Dimensional Discrete Tree Graphical Models

no code implementations NeurIPS 2014 Chao Chen, Han Liu, Dimitris Metaxas, Tianqi Zhao

Though the mode finding problem is generally intractable in high dimensions, this paper unveils that, if the distribution can be approximated well by a tree graphical model, mode characterization is significantly easier.

A New Framework for Sign Language Recognition based on 3D Handshape Identification and Linguistic Modeling

no code implementations LREC 2014 Mark Dilsizian, Polina Yanovich, Shu Wang, Carol Neidle, Dimitris Metaxas

Current approaches to sign recognition by computer generally have at least some of the following limitations: they rely on laboratory conditions for sign production, are limited to a small vocabulary, rely on 2D modeling (and therefore cannot deal with occlusions and off-plane rotations), and/or achieve limited success.

3D Reconstruction Sign Language Recognition +1

Handling Noise in Single Image Deblurring Using Directional Filters

no code implementations CVPR 2013 Lin Zhong, Sunghyun Cho, Dimitris Metaxas, Sylvain Paris, Jue Wang

Based on this observation, our method applies a series of directional filters at different orientations to the input image, and estimates an accurate Radon transform of the blur kernel from each filtered image.

Deblurring Image Deblurring +2

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