Search Results for author: Ho Hin Lee

Found 12 papers, 2 papers with code

Pseudo-Label Guided Multi-Contrast Generalization for Non-Contrast Organ-Aware Segmentation

no code implementations12 May 2022 Ho Hin Lee, Yucheng Tang, Riqiang Gao, Qi Yang, Xin Yu, Shunxing Bao, James G. Terry, J. Jeffrey Carr, Yuankai Huo, Bennett A. Landman

In this paper, we propose a novel unsupervised approach that leverages pairwise contrast-enhanced CT (CECT) context to compute non-contrast segmentation without ground-truth label.

Characterizing Renal Structures with 3D Block Aggregate Transformers

no code implementations4 Mar 2022 Xin Yu, Yucheng Tang, Yinchi Zhou, Riqiang Gao, Qi Yang, Ho Hin Lee, Thomas Li, Shunxing Bao, Yuankai Huo, Zhoubing Xu, Thomas A. Lasko, Richard G. Abramson, Bennett A. Landman

Efficiently quantifying renal structures can provide distinct spatial context and facilitate biomarker discovery for kidney morphology.

Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging

no code implementations MICCAI Workshop COMPAY 2021 Shunxing Bao, Yucheng Tang, Ho Hin Lee, Riqiang Gao, Sophie Chiron, Ilwoo Lyu, Lori A. Coburn, Keith T. Wilson, Joseph T. Roland, Bennett A. Landman, Yuankai Huo

Our contribution is three-fold: (1) a single deep network framework is proposed to tackle missing stain in MxIF; (2) the proposed 'N-to-N' strategy reduces theoretical four years of computational time to 20 hours when covering all possible missing stains scenarios, with up to five missing stains (e. g., '(N-1)-to-1', '(N-2)-to-2'); and (3) this work is the first comprehensive experimental study of investigating cross-stain synthesis in MxIF.

Image Generation

Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective

no code implementations25 Jul 2021 Riqiang Gao, Yucheng Tang, Kaiwen Xu, Ho Hin Lee, Steve Deppen, Kim Sandler, Pierre Massion, Thomas A. Lasko, Yuankai Huo, Bennett A. Landman

To our knowledge, it is the first generative adversarial model that addresses multi-modal missing imputation by modeling the joint distribution of image and non-image data.

Imputation

RAP-Net: Coarse-to-Fine Multi-Organ Segmentation with Single Random Anatomical Prior

1 code implementation23 Dec 2020 Ho Hin Lee, Yucheng Tang, Shunxing Bao, Richard G. Abramson, Yuankai Huo, Bennett A. Landman

We combine the anatomical prior with corresponding extracted patches to preserve the anatomical locations and boundary information for performing high-resolution segmentation across all organs in a single model.

Outlier Guided Optimization of Abdominal Segmentation

no code implementations10 Feb 2020 Yuchen Xu, Olivia Tang, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman

We built on a pre-trained 3D U-Net model for abdominal multi-organ segmentation and augmented the dataset either with outlier data (e. g., exemplars for which the baseline algorithm failed) or inliers (e. g., exemplars for which the baseline algorithm worked).

Active Learning Computed Tomography (CT)

Contrast Phase Classification with a Generative Adversarial Network

no code implementations14 Nov 2019 Yucheng Tang, Ho Hin Lee, Yuchen Xu, Olivia Tang, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Camilo Bermudez, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman

Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context of underlying anatomy.

Classification Computed Tomography (CT) +2

Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision

no code implementations12 Nov 2019 Ho Hin Lee, Yucheng Tang, Olivia Tang, Yuchen Xu, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman

The contributions of the proposed method are threefold: We show that (1) the QA scores can be used as a loss function to perform semi-supervised learning for unlabeled data, (2) the well trained discriminator is learnt by QA score rather than traditional true/false, and (3) the performance of multi-organ segmentation on unlabeled datasets can be fine-tuned with more robust and higher accuracy than the original baseline method.

Medical Image Segmentation Semantic Segmentation

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