Search Results for author: Chuyang Ye

Found 15 papers, 3 papers with code

Unsupervised Brain Tumor Segmentation with Image-based Prompts

no code implementations4 Apr 2023 Xinru Zhang, Ni Ou, Chenghao Liu, Zhizheng Zhuo, Yaou Liu, Chuyang Ye

Specifically, instead of directly training a model for brain tumor segmentation with a large amount of annotated data, we seek to train a model that can answer the question: is a voxel in the input image associated with tumor-like hyper-/hypo-intensity?

Brain Tumor Segmentation Lesion Segmentation +1

Fiber Tract Shape Measures Inform Prediction of Non-Imaging Phenotypes

no code implementations16 Mar 2023 Wan Liu, Yuqian Chen, Chuyang Ye, Nikos Makris, Yogesh Rathi, Weidong Cai, Fan Zhang, Lauren J. O'Donnell

In this paper, we investigate the potential of fiber tract shape features for predicting non-imaging phenotypes, both individually and in combination with traditional features.

One-Shot Segmentation of Novel White Matter Tracts via Extensive Data Augmentation

1 code implementation13 Mar 2023 Wan Liu, Qi Lu, Zhizheng Zhuo, Yaou Liu, Chuyang Ye

However, accurate segmentation of novel WM tracts can still be challenging in the one-shot setting, where only one scan is annotated for the novel WM tracts.

Data Augmentation One-Shot Segmentation +1

Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations

1 code implementation16 Feb 2023 Zipei Zhao, Fengqian Pang, Yaou Liu, Zhiwen Liu, Chuyang Ye

Typically, to train CNN-based cell detection models, every positive instance in the training images needs to be annotated, and instances that are not labeled as positive are considered negative samples.

Cell Detection

CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation

1 code implementation16 Aug 2021 Xinru Zhang, Chenghao Liu, Ni Ou, Xiangzhu Zeng, Xiaoliang Xiong, Yizhou Yu, Zhiwen Liu, Chuyang Ye

Data augmentation is a widely used strategy that improves the training of CNNs, and the design of the augmentation method for brain lesion segmentation is still an open problem.

Data Augmentation Lesion Segmentation

Positive-unlabeled Learning for Cell Detection in Histopathology Images with Incomplete Annotations

no code implementations30 Jun 2021 Zipei Zhao, Fengqian Pang, Zhiwen Liu, Chuyang Ye

Usually, incomplete annotations can be achieved, where positive labeling results are carefully examined to ensure their reliability but there can be other positive instances, i. e., cells of interest, that are not included in the annotations.

Cell Detection Mitosis Detection

Knowledge Transfer for Few-shot Segmentation of Novel White Matter Tracts

no code implementations30 May 2021 Qi Lu, Chuyang Ye

The expensive manual delineation can be a particular disadvantage when novel WM tracts, i. e., tracts that have not been included in existing manual delineations, are to be analyzed.

Transfer Learning

Segmentation-based Method combined with Dynamic Programming for Brain Midline Delineation

no code implementations27 Feb 2020 Shen Wang, Kongming Liang, Chengwei Pan, Chuyang Ye, Xiuli Li, Feng Liu, Yizhou Yu, Yizhou Wang

The midline related pathological image features are crucial for evaluating the severity of brain compression caused by stroke or traumatic brain injury (TBI).

Decision Making

Knowledge Transfer between Datasets for Learning-based Tissue Microstructure Estimation

no code implementations24 Oct 2019 Yu Qin, Yuxing Li, Zhiwen Liu, Chuyang Ye

Then, the interpolated signals are used together with the high-quality tissue microstructure computed from the source dataset to train deep networks that perform tissue microstructure estimation for the target dataset.

Transfer Learning

Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model

no code implementations4 Mar 2019 Wenhui Cui, Yanlin Liu, Yuxing Li, Menghao Guo, Yiming Li, Xiuli Li, Tianle Wang, Xiangzhu Zeng, Chuyang Ye

Since unannotated data is generally abundant, it is desirable to use unannotated data to improve the segmentation performance for CNNs when limited annotated data is available.

Image Classification Ischemic Stroke Lesion Segmentation +1

Learning-based Ensemble Average Propagator Estimation

no code implementations20 Jun 2017 Chuyang Ye

The diffusion profile can be described by the ensemble average propagator (EAP), which is inferred from observed diffusion signals.

Fiber Orientation Estimation Guided by a Deep Network

no code implementations19 May 2017 Chuyang Ye, Jerry L. Prince

In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN).

Estimation of Tissue Microstructure Using a Deep Network Inspired by a Sparse Reconstruction Framework

no code implementations5 Apr 2017 Chuyang Ye

In this work, we propose a deep network based approach to the NODDI microstructure estimation, which is named Microstructure Estimation using a Deep Network (MEDN).

Estimation of Fiber Orientations Using Neighborhood Information

no code implementations16 Jan 2016 Chuyang Ye, Jiachen Zhuo, Rao P. Gullapalli, Jerry L. Prince

Data from diffusion magnetic resonance imaging (dMRI) can be used to reconstruct fiber tracts, for example, in muscle and white matter.

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