no code implementations • 15 Aug 2024 • Chuyang Ye, Dongyan Wei, Zhendong Liu, Yuanyi Pang, Yixi Lin, Jiarong Liao, Qinting Jiang, Xianghua Fu, Qing Li, Jingyan Jiang
It features three key components: Diversity Discrimination (DD) to assess batch diversity, Diversity Adaptive Batch Normalization (DABN) to tailor normalization methods based on DD insights, and Diversity Adaptive Fine-Tuning (DAFT) to selectively fine-tune the model.
1 code implementation • 23 Jun 2024 • Lujun Gui, Chuyang Ye, Tianyi Yan
As the enhancement of image intensity grows with a higher dose of contrast agents, we assume that it is less challenging to synthesize a virtual image with a lower dose, where the difference between the contrast-enhanced and non-contrast images is smaller.
no code implementations • 8 Jun 2024 • Qinting Jiang, Chuyang Ye, Dongyan Wei, Yuan Xue, Jingyan Jiang, Zhi Wang
Specifically, Our DYN consists of layer-wise instance statistics clustering (LISC) and cluster-aware batch normalization (CABN).
1 code implementation • 16 May 2024 • Xinru Zhang, Ni Ou, Berke Doga Basaran, Marco Visentin, Mengyun Qiao, Renyang Gu, Cheng Ouyang, Yaou Liu, Paul M. Matthew, Chuyang Ye, Wenjia Bai
In this work, we propose a universal foundation model for 3D brain lesion segmentation, which can automatically segment different types of brain lesions for input data of various imaging modalities.
no code implementations • 2 Feb 2024 • Ruizhi Zhu, Xinru Zhang, Haowen Pang, Chundan Xu, Chuyang Ye
Synthesizing healthy brain scans from diseased brain scans offers a potential solution to address the limitations of general-purpose algorithms, such as tissue segmentation and brain extraction algorithms, which may not effectively handle diseased images.
no code implementations • 25 Sep 2023 • Wan Liu, Chuyang Ye
Since both of them lead to different signal-to-noise ratios (SNRs) between the training and test data, we propose to augment the training scans by adjusting the noise magnitude and develop an adapted residual bootstrap strategy for the augmentation.
no code implementations • 4 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?
no code implementations • 16 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.
1 code implementation • 13 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.
1 code implementation • 16 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.
1 code implementation • 13 May 2022 • Tianshu Zheng, Cong Sun, Weihao Zheng, Wen Shi, Haotian Li, Yi Sun, Yi Zhang, Guangbin Wang, Chuyang Ye, Dan Wu
Thus, the METSC is composed with three stages, an embedding stage, a sparse representation stage, and a mapping stage.
1 code implementation • 16 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.
no code implementations • 30 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.
no code implementations • 30 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.
no code implementations • 27 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).
no code implementations • 24 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.
1 code implementation • 4 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.
no code implementations • 20 Jun 2017 • Chuyang Ye
The diffusion profile can be described by the ensemble average propagator (EAP), which is inferred from observed diffusion signals.
no code implementations • 19 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).
no code implementations • 5 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).
no code implementations • 16 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.