Search Results for author: Pew-Thian Yap

Found 29 papers, 4 papers with code

Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework

no code implementations10 Feb 2024 Mengqi Wu, Lintao Zhang, Pew-Thian Yap, Hongtu Zhu, Mingxia Liu

The SST utilizes an energy-based model to comprehend the global latent distribution of a target domain and translate source latent codes toward the target domain, while SMS enables MRI synthesis with a target-specific style.

Image Generation Translation

Towards Architecture-Insensitive Untrained Network Priors for Accelerated MRI Reconstruction

no code implementations15 Dec 2023 Yilin Liu, Yunkui Pang, Jiang Li, Yong Chen, Pew-Thian Yap

Untrained neural networks pioneered by Deep Image Prior (DIP) have recently enabled MRI reconstruction without requiring fully-sampled measurements for training.

MRI Reconstruction

Federated Learning for Medical Image Analysis: A Survey

no code implementations9 Jun 2023 Hao Guan, Pew-Thian Yap, Andrea Bozoki, Mingxia Liu

In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches.

Federated Learning

The Devil is in the Upsampling: Architectural Decisions Made Simpler for Denoising with Deep Image Prior

2 code implementations ICCV 2023 Yilin Liu, Jiang Li, Yunkui Pang, Dong Nie, Pew-Thian Yap

Existing methods mostly handcraft or search for the architecture from a large design space, due to the lack of understanding on how the architectural choice corresponds to the image.

Image Denoising

Brain Tissue Segmentation Across the Human Lifespan via Supervised Contrastive Learning

no code implementations3 Jan 2023 Xiaoyang Chen, Jinjian Wu, Wenjiao Lyu, Yicheng Zou, Kim-Han Thung, Siyuan Liu, Ye Wu, Sahar Ahmad, Pew-Thian Yap

In this paper, we make the first attempt to segment brain tissues across the entire human lifespan (0-100 years of age) using a unified deep learning model.

Contrastive Learning Segmentation +1

Source-Free Unsupervised Domain Adaptation: A Survey

no code implementations31 Dec 2022 Yuqi Fang, Pew-Thian Yap, Weili Lin, Hongtu Zhu, Mingxia Liu

Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and transmission cost, and computation burden.

Transfer Learning Unsupervised Domain Adaptation

Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework

1 code implementation30 Jun 2022 Haoran Dou, Luyi Han, Yushuang He, Jun Xu, Nishant Ravikumar, Ritse Mann, Alejandro F. Frangi, Pew-Thian Yap, Yunzhi Huang

Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy.

Deformable Registration of Brain MR Images via a Hybrid Loss

no code implementations28 Oct 2021 Luyi Han, Haoran Dou, Yunzhi Huang, Pew-Thian Yap

Unsupervised learning strategy is widely adopted by the deformable registration models due to the lack of ground truth of deformation fields.

Dual Shape Guided Segmentation Network for Organs-at-Risk in Head and Neck CT Images

no code implementations23 Oct 2021 Shuai Wang, Theodore Yanagihara, Bhishamjit Chera, Colette Shen, Pew-Thian Yap, Jun Lian

To deal with the large shape variation and unclear boundary of OARs in CT images, we represent the organ shape using an organ-specific unilateral inverse-distance map (UIDM) and guide the segmentation task from two different perspectives: direct shape guidance by following the segmentation prediction and across shape guidance by sharing the segmentation feature.


Learning MRI Artifact Removal With Unpaired Data

no code implementations9 Oct 2021 Siyuan Liu, Kim-Han Thung, Liangqiong Qu, Weili Lin, Dinggang Shen, Pew-Thian Yap

Retrospective artifact correction (RAC) improves image quality post acquisition and enhances image usability.

SkullEngine: A Multi-stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection

no code implementations7 Oct 2021 Qin Liu, Han Deng, Chunfeng Lian, Xiaoyang Chen, Deqiang Xiao, Lei Ma, Xu Chen, Tianshu Kuang, Jaime Gateno, Pew-Thian Yap, James J. Xia

We propose a multi-stage coarse-to-fine CNN-based framework, called SkullEngine, for high-resolution segmentation and large-scale landmark detection through a collaborative, integrated, and scalable JSD model and three segmentation and landmark detection refinement models.

Image Segmentation Segmentation +1

Learning Multi-Site Harmonization of Magnetic Resonance Images Without Traveling Human Phantoms

no code implementations30 Sep 2021 Siyuan Liu, Pew-Thian Yap

Harmonization improves data consistency and is central to effective integration of diverse imaging data acquired across multiple sites.

Real-Time Mapping of Tissue Properties for Magnetic Resonance Fingerprinting

no code implementations16 Jul 2021 Yilin Liu, Yong Chen, Pew-Thian Yap

Magnetic resonance Fingerprinting (MRF) is a relatively new multi-parametric quantitative imaging method that involves a two-step process: (i) reconstructing a series of time frames from highly-undersampled non-Cartesian spiral k-space data and (ii) pattern matching using the time frames to infer tissue properties (e. g., T1 and T2 relaxation times).

Magnetic Resonance Fingerprinting

Navigator-Free Submillimeter Diffusion Imaging using Multishot-encoded Simultaneous Multi-slice (MUSIUM)

no code implementations1 Dec 2020 Wei-Tang Chang, Khoi Minh Huynh, Pew-Thian Yap, Weili Lin

In this work, we developed a navigator-free multishot-encoded simultaneous multi-slice (MUSIUM) imaging approach on a 3T MR scanner, achieving enhanced SNR, low RF power and peak amplitude, and being free from slab boundary artifacts.

Medical Physics Image and Video Processing

Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces

1 code implementation6 Sep 2020 Peirong Liu, Zhengwang Wu, Gang Li, Pew-Thian Yap, Dinggang Shen

Charting cortical growth trajectories is of paramount importance for understanding brain development.

Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE)

no code implementations25 Feb 2020 Yoonmi Hong, Wei-Tang Chang, Geng Chen, Ye Wu, Weili Lin, Dinggang Shen, Pew-Thian Yap

Diffusion MRI (dMRI) is a unique imaging technique for in vivo characterization of tissue microstructure and white matter pathways.

Multi-Kernel Filtering for Nonstationary Noise: An Extension of Bilateral Filtering Using Image Context

no code implementations17 Aug 2019 Feihong Liu, Jun Feng, Pew-Thian Yap, Dinggang Shen

Next, a leaf cluster is used to generate one of the multiple kernels, and two corresponding predecessor clusters are used to fine-tune the adopted kernel.

Clustering Denoising +1

Synthesis and Inpainting-Based MR-CT Registration for Image-Guided Thermal Ablation of Liver Tumors

no code implementations30 Jul 2019 Dongming Wei, Sahar Ahmad, Jiayu Huo, Wen Peng, Yunhao Ge, Zhong Xue, Pew-Thian Yap, Wentao Li, Dinggang Shen, Qian Wang

Then, an unsupervised registration network is used to efficiently align the pre-procedural CT (pCT) with the inpainted iCT (inpCT) image.

Image Registration

DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks

no code implementations7 Jun 2019 Feihong Liu, Jun Feng, Geng Chen, Ye Wu, Yoonmi Hong, Pew-Thian Yap, Dinggang Shen

GCNNs are capable of extracting the geometric features of each fiber tract and harnessing the resulting features for accurate fiber parcellation and ultimately avoiding the use of atlases and any registration method.

BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks

no code implementations13 Feb 2018 Jingfan Fan, Xiaohuan Cao, Pew-Thian Yap, Dinggang Shen

In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance.

Image Registration

Regularized Spherical Polar Fourier Diffusion MRI with Optimal Dictionary Learning

no code implementations2 Jul 2013 Jian Cheng, Tianzi Jiang, Rachid Deriche, Dinggang Shen, Pew-Thian Yap

Compared with the start-of-the-art CR-DL and DR-DL methods proposed by Merlet et al. and Bilgic et al., espectively, our work offers the following advantages.

Dictionary Learning

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