Search Results for author: Yucheng Tang

Found 46 papers, 18 papers with code

More Knowledge is Better: Cross-Modality Volume Completion and 3D+2D Segmentation for Intracardiac Echocardiography Contouring

no code implementations9 Dec 2018 Haofu Liao, Yucheng Tang, Gareth Funka-Lea, Jiebo Luo, Shaohua Kevin Zhou

Using catheter ablation to treat atrial fibrillation increasingly relies on intracardiac echocardiography (ICE) for an anatomical delineation of the left atrium and the pulmonary veins that enter the atrium.

Anatomy

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.

Image Segmentation Medical Image Segmentation +3

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.

Anatomy Classification +4

Internal-transfer Weighting of Multi-task Learning for Lung Cancer Detection

1 code implementation16 Dec 2019 Yiyuan Yang, Riqiang Gao, Yucheng Tang, Sanja L. Antic, Steve Deppen, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman

To improve performance on the primary task, we propose an Internal-Transfer Weighting (ITW) strategy to suppress the loss functions on auxiliary tasks for the final stages of training.

Multi-Task Learning

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) +2

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.

Organ Segmentation Segmentation

UNETR: Transformers for 3D Medical Image Segmentation

10 code implementations18 Mar 2021 Ali Hatamizadeh, Yucheng Tang, Vishwesh Nath, Dong Yang, Andriy Myronenko, Bennett Landman, Holger Roth, Daguang Xu

Inspired by the recent success of transformers for Natural Language Processing (NLP) in long-range sequence learning, we reformulate the task of volumetric (3D) medical image segmentation as a sequence-to-sequence prediction problem.

3D Medical Imaging Segmentation Image Segmentation +3

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

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.

Generative Adversarial Network Image Generation +1

Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis

1 code implementation CVPR 2022 Yucheng Tang, Dong Yang, Wenqi Li, Holger Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, Ali Hatamizadeh

Vision Transformers (ViT)s have shown great performance in self-supervised learning of global and local representations that can be transferred to downstream applications.

 Ranked #1 on Medical Image Segmentation on Synapse multi-organ CT (using extra training data)

Anatomy Computed Tomography (CT) +3

Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

2 code implementations4 Jan 2022 Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger Roth, Daguang Xu

Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression of the malignant entity.

3D Semantic Segmentation Brain Tumor Segmentation +2

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.

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.

Organ Segmentation Pseudo Label +1

Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography

1 code implementation4 Sep 2022 Thomas Z. Li, Kaiwen Xu, Riqiang Gao, Yucheng Tang, Thomas A. Lasko, Fabien Maldonado, Kim Sandler, Bennett A. Landman

In cross-validation on screening chest CTs from the NLST, our methods (0. 785 and 0. 786 AUC respectively) significantly outperform a cross-sectional approach (0. 734 AUC) and match the discriminative performance of the leading longitudinal medical imaging algorithm (0. 779 AUC) on benign versus malignant classification.

Lung Cancer Diagnosis Time Series Analysis

Longitudinal Variability Analysis on Low-dose Abdominal CT with Deep Learning-based Segmentation

no code implementations28 Sep 2022 Xin Yu, Yucheng Tang, Qi Yang, Ho Hin Lee, Riqiang Gao, Shunxing Bao, Ann Zenobia Moore, Luigi Ferrucci, Bennett A. Landman

Metabolic health is increasingly implicated as a risk factor across conditions from cardiology to neurology, and efficiency assessment of body composition is critical to quantitatively characterizing these relationships.

Computed Tomography (CT) Segmentation

UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation

1 code implementation28 Sep 2022 Xin Yu, Qi Yang, Yinchi Zhou, Leon Y. Cai, Riqiang Gao, Ho Hin Lee, Thomas Li, Shunxing Bao, Zhoubing Xu, Thomas A. Lasko, Richard G. Abramson, Zizhao Zhang, Yuankai Huo, Bennett A. Landman, Yucheng Tang

Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis.

Brain Segmentation Image Segmentation +3

Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models

1 code implementation28 Sep 2022 Xin Yu, Qi Yang, Yucheng Tang, Riqiang Gao, Shunxing Bao, LeonY. Cai, Ho Hin Lee, Yuankai Huo, Ann Zenobia Moore, Luigi Ferrucci, Bennett A. Landman

External experiments on 20 subjects from the Baltimore Longitudinal Study of Aging (BLSA) dataset that contains longitudinal single abdominal slices validate that our method can harmonize the slice positional variance in terms of muscle and visceral fat area.

Computed Tomography (CT)

Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation

1 code implementation16 Oct 2022 Ho Hin Lee, Yucheng Tang, Han Liu, Yubo Fan, Leon Y. Cai, Qi Yang, Xin Yu, Shunxing Bao, Yuankai Huo, Bennett A. Landman

We evaluate our proposed approach on multi-organ segmentation with both non-contrast CT (NCCT) datasets and the MICCAI 2015 BTCV Challenge contrast-enhance CT (CECT) datasets.

Computed Tomography (CT) Contrastive Learning +1

CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection

2 code implementations ICCV 2023 Jie Liu, Yixiao Zhang, Jie-Neng Chen, Junfei Xiao, Yongyi Lu, Bennett A. Landman, Yixuan Yuan, Alan Yuille, Yucheng Tang, Zongwei Zhou

The proposed model is developed from an assembly of 14 datasets, using a total of 3, 410 CT scans for training and then evaluated on 6, 162 external CT scans from 3 additional datasets.

Organ Segmentation Segmentation +1

Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning

1 code implementation31 May 2023 Ruining Deng, Yanwei Li, Peize Li, Jiacheng Wang, Lucas W. Remedios, Saydolimkhon Agzamkhodjaev, Zuhayr Asad, Quan Liu, Can Cui, Yaohong Wang, Yihan Wang, Yucheng Tang, Haichun Yang, Yuankai Huo

The contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators; (2) The proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists; (3) A deep corrective learning (learning with imperfect label) method is proposed to further improve the segmentation performance using partially annotated noisy data.

Cell Segmentation Image Segmentation +3

Multi-Contrast Computed Tomography Atlas of Healthy Pancreas

no code implementations2 Jun 2023 Yinchi Zhou, Ho Hin Lee, Yucheng Tang, Xin Yu, Qi Yang, Shunxing Bao, Jeffrey M. Spraggins, Yuankai Huo, Bennett A. Landman

Briefly, DEEDs affine and non-rigid registration are performed to transfer patient abdominal volumes to a fixed high-resolution atlas template.

Anatomy Computed Tomography (CT)

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images

no code implementations3 Jul 2023 Can Cui, Yaohong Wang, Shunxing Bao, Yucheng Tang, Ruining Deng, Lucas W. Remedios, Zuhayr Asad, Joseph T. Roland, Ken S. Lau, Qi Liu, Lori A. Coburn, Keith T. Wilson, Bennett A. Landman, Yuankai Huo

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training.

Anomaly Detection

Leverage Weakly Annotation to Pixel-wise Annotation via Zero-shot Segment Anything Model for Molecular-empowered Learning

no code implementations10 Aug 2023 Xueyuan Li, Ruining Deng, Yucheng Tang, Shunxing Bao, Haichun Yang, Yuankai Huo

In this paper, we explore the potential of bypassing pixel-level delineation by employing the recent segment anything model (SAM) on weak box annotation in a zero-shot learning approach.

Image Segmentation Segmentation +2

High-performance Data Management for Whole Slide Image Analysis in Digital Pathology

1 code implementation10 Aug 2023 Haoju Leng, Ruining Deng, Shunxing Bao, Dazheng Fang, Bryan A. Millis, Yucheng Tang, Haichun Yang, Xiao Wang, Yifan Peng, Lipeng Wan, Yuankai Huo

The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario ("CPU scenario"), and (2) a GPU-based deep learning framework scenario ("GPU scenario").

Management whole slide images

Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration

1 code implementation8 Sep 2023 Xin Yu, Yucheng Tang, Qi Yang, Ho Hin Lee, Shunxing Bao, Yuankai Huo, Bennett A. Landman

Subsequently, the model is finetuned with 45 T1w 3D volumes from Open Access Series Imaging Studies (OASIS) where both 133 whole brain classes and TICV/PFV labels are available.

Brain Segmentation Segmentation

Deep conditional generative models for longitudinal single-slice abdominal computed tomography harmonization

1 code implementation17 Sep 2023 Xin Yu, Qi Yang, Yucheng Tang, Riqiang Gao, Shunxing Bao, Leon Y. Cai, Ho Hin Lee, Yuankai Huo, Ann Zenobia Moore, Luigi Ferrucci, Bennett A. Landman

We further evaluate our method's capability to harmonize longitudinal positional variation on 1033 subjects from the Baltimore Longitudinal Study of Aging (BLSA) dataset, which contains longitudinal single abdominal slices, and confirmed that our method can harmonize the slice positional variance in terms of visceral fat area.

Computed Tomography (CT)

Cannot find the paper you are looking for? You can Submit a new open access paper.