no code implementations • 5 Jun 2023 • Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, Michael Lingzhi Li
To the best of our knowledge, CMExam is the first Chinese medical exam dataset to provide comprehensive medical annotations.
1 code implementation • 26 May 2023 • Suet-Ying Lam, Qingcheng Zeng, Kexun Zhang, Chenyu You, Rob Voigt
Recent psycholinguistic studies suggest that humans adapt their referential biases with recent exposure to referential patterns; closely replicating three relevant psycholinguistic experiments from Johnson & Arnold (2022) in an in-context learning (ICL) framework, we found that InstructGPT adapts its pronominal interpretations in response to the frequency of referential patterns in the local discourse, though in a limited fashion: adaptation was only observed relative to syntactic but not semantic biases.
no code implementations • 8 Apr 2023 • Kun Han, Yifeng Xiong, Chenyu You, Pooya Khosravi, Shanlin Sun, Xiangyi Yan, James Duncan, Xiaohui Xie
Then, we use an image sequence generator and semantic diffusion refiner conditioned on the generated mask sequences to produce realistic 3D medical images that align with the generated masks.
no code implementations • 6 Apr 2023 • Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, James S. Duncan
Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation.
no code implementations • 6 Apr 2023 • Xiangyi Yan, Junayed Naushad, Chenyu You, Hao Tang, Shanlin Sun, Kun Han, Haoyu Ma, James Duncan, Xiaohui Xie
In this paper, we propose a novel contrastive learning framework that integrates Localized Region Contrast (LRC) to enhance existing self-supervised pre-training methods for medical image segmentation.
1 code implementation • 5 Apr 2023 • Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, Jasjeet S. Sekhon, James S. Duncan
In this work, we present ACTION++, an improved contrastive learning framework with adaptive anatomical contrast for semi-supervised medical segmentation.
no code implementations • 16 Feb 2023 • Nuo Chen, Linjun Shou, Ming Gong, Jian Pei, Chenyu You, Jianhui Chang, Daxin Jiang, Jia Li
For instance, TPLMs jointly pre-trained with table and text input could be effective for tasks also with table-text joint input like table question answering, but it may fail for tasks with only tables or text as input such as table retrieval.
1 code implementation • 3 Feb 2023 • Chenyu You, Weicheng Dai, Yifei Min, Fenglin Liu, David A. Clifton, S Kevin Zhou, Lawrence Hamilton Staib, James S Duncan
For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pairs of samples.
no code implementations • 22 Nov 2022 • Fenglin Liu, Xian Wu, Chenyu You, Shen Ge, Yuexian Zou, Xu sun
To this end, we introduce the unpaired video captioning task aiming to train models without coupled video-caption pairs in target language.
2 code implementations • 23 Oct 2022 • Fenglin Liu, Bang Yang, Chenyu You, Xian Wu, Shen Ge, Zhangdaihong Liu, Xu sun, Yang Yang, David A. Clifton
We build a benchmark clinical dataset and propose the Re3Writer, which imitates the working patterns of physicians to first retrieve related working experience from historical PIs written by physicians, then reason related medical knowledge.
no code implementations • 27 Sep 2022 • Chenyu You, Weicheng Dai, Fenglin Liu, Yifei Min, Haoran Su, Xiaoran Zhang, Xiaoxiao Li, David A. Clifton, Lawrence Staib, James S. Duncan
Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention.
1 code implementation • 1 Sep 2022 • Xiaoran Zhang, Chenyu You, Shawn Ahn, Juntang Zhuang, Lawrence Staib, James Duncan
Learning spatial-temporal correspondences in cardiac motion from images is important for understanding the underlying dynamics of cardiac anatomical structures.
no code implementations • 18 Aug 2022 • Nuo Chen, Chenyu You
To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the coarse-grained representations of the source sequences, i. e., passage and question.
1 code implementation • 20 Jul 2022 • Siyuan Dong, Gilbert Hangel, Eric Z. Chen, Shanhui Sun, Wolfgang Bogner, Georg Widhalm, Chenyu You, John A. Onofrey, Robin de Graaf, James S. Duncan
Specifically, we propose a flow-based enhancer network to improve the visual quality of super-resolution MRSI.
no code implementations • 17 Jun 2022 • Siyuan Dong, Gilbert Hangel, Wolfgang Bogner, Georg Widhalm, Karl Rössler, Siegfried Trattnig, Chenyu You, Robin de Graaf, John Onofrey, James Duncan
Magnetic Resonance Spectroscopic Imaging (MRSI) is a valuable tool for studying metabolic activities in the human body, but the current applications are limited to low spatial resolutions.
no code implementations • 10 Jun 2022 • Fenglin Liu, Bang Yang, Chenyu You, Xian Wu, Shen Ge, Adelaide Woicik, Sheng Wang
This task aims to automatically generate a sentence that describes the function of a GO term belonging to one of the three categories, i. e., molecular function, biological process, and cellular component.
no code implementations • 6 Jun 2022 • Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, James S. Duncan
In this work, we present ACTION, an Anatomical-aware ConTrastive dIstillatiON framework, for semi-supervised medical image segmentation.
no code implementations • 3 Jun 2022 • Chenyu You, Jinlin Xiang, Kun Su, Xiaoran Zhang, Siyuan Dong, John Onofrey, Lawrence Staib, James S. Duncan
Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model that (1) performs better on all these datasets, and (2) generalizes well and transfers better to the unknown target site domain.
no code implementations • Findings (NAACL) 2022 • Chenyu You, Nuo Chen, Fenglin Liu, Shen Ge, Xian Wu, Yuexian Zou
To evaluate the capacity of SCQA systems in a dialogue-style interaction, we assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with more than 40k question-answer pairs from 4k conversations.
Ranked #1 on
Spoken Language Understanding
on Spoken-SQuAD
Conversational Question Answering
Spoken Language Understanding
+1
no code implementations • 25 Feb 2022 • Kun Han, Shanlin Sun, Xiangyi Yan, Chenyu You, Hao Tang, Junayed Naushad, Haoyu Ma, Deying Kong, Xiaohui Xie
Here we propose a new optimization-based method named DNVF (Diffeomorphic Image Registration with Neural Velocity Field) which utilizes deep neural network to model the space of admissible transformations.
no code implementations • 26 Jan 2022 • Chenyu You, Ruihan Zhao, Fenglin Liu, Siyuan Dong, Sandeep Chinchali, Ufuk Topcu, Lawrence Staib, James S. Duncan
In this work, we present CASTformer, a novel type of adversarial transformers, for 2D medical image segmentation.
1 code implementation • 3 Jan 2022 • Aosong Feng, Chenyu You, Shiqiang Wang, Leandros Tassiulas
We also show that the trained graph filters in KerGNNs can reveal the local graph structures of the dataset, which significantly improves the model interpretability compared with conventional GNN models.
no code implementations • NeurIPS 2021 • Fenglin Liu, Chenyu You, Xian Wu, Shen Ge, Sheng Wang, Xu sun
KGAE consists of a pre-constructed knowledge graph, a knowledge-driven encoder and a knowledge-driven decoder.
no code implementations • 28 Oct 2021 • Chenyu You, Lianyi Han, Aosong Feng, Ruihan Zhao, Hui Tang, Wei Fan
Space-time video super-resolution (STVSR) aims to construct a high space-time resolution video sequence from the corresponding low-frame-rate, low-resolution video sequence.
no code implementations • 29 Sep 2021 • Haoyu Ma, Yifan Huang, Tianlong Chen, Hao Tang, Chenyu You, Zhangyang Wang, Xiaohui Xie
However, it is unclear why the distorted distribution of the logits is catastrophic to the student model.
no code implementations • Findings (EMNLP) 2021 • Chenyu You, Nuo Chen, Yuexian Zou
In this paper, we propose novel training schemes for spoken question answering with a self-supervised training stage and a contrastive representation learning stage.
no code implementations • 13 Aug 2021 • Chenyu You, Yuan Zhou, Ruihan Zhao, Lawrence Staib, James S. Duncan
However, most existing learning-based approaches usually suffer from limited manually annotated medical data, which poses a major practical problem for accurate and robust medical image segmentation.
no code implementations • 4 Jun 2021 • Nuo Chen, Chenyu You, Yuexian Zou
We also utilize the proposed self-supervised learning tasks to capture intra-sentence coherence.
1 code implementation • ICLR 2021 • Haoyu Ma, Tianlong Chen, Ting-Kuei Hu, Chenyu You, Xiaohui Xie, Zhangyang Wang
Knowledge Distillation (KD) is a widely used technique to transfer knowledge from pre-trained teacher models to (usually more lightweight) student models.
no code implementations • 14 May 2021 • Chenyu You, Ruihan Zhao, Lawrence Staib, James S. Duncan
In this work, we present a novel Contrastive Voxel-wise Representation Learning (CVRL) method to effectively learn low-level and high-level features by capturing 3D spatial context and rich anatomical information along both the feature and the batch dimensions.
no code implementations • 20 Dec 2020 • Nuo Chen, Fenglin Liu, Chenyu You, Peilin Zhou, Yuexian Zou
To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the \textit{coarse-grained} representations of the source sequences, i. e., passage and question.
no code implementations • 21 Oct 2020 • Chenyu You, Nuo Chen, Yuexian Zou
Spoken conversational question answering (SCQA) requires machines to model complex dialogue flow given the speech utterances and text corpora.
Audio Signal Processing
Conversational Question Answering
+2
no code implementations • 21 Oct 2020 • Chenyu You, Nuo Chen, Yuexian Zou
However, the recent work shows that ASR systems generate highly noisy transcripts, which critically limit the capability of machine comprehension on the SQA task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
no code implementations • 18 Oct 2020 • Chenyu You, Nuo Chen, Fenglin Liu, Dongchao Yang, Yuexian Zou
In spoken question answering, QA systems are designed to answer questions from contiguous text spans within the related speech transcripts.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
no code implementations • 6 Sep 2020 • Chenyu You, Junlin Yang, Julius Chapiro, James S. Duncan
However, the well-trained models often fail in the target domain due to the domain shift.
no code implementations • 16 May 2020 • Fenglin Liu, Xuancheng Ren, Guangxiang Zhao, Chenyu You, Xuewei Ma, Xian Wu, Xu sun
While it is common practice to draw information from only the last encoder layer, recent work has proposed to use representations from different encoder layers for diversified levels of information.
no code implementations • 26 Nov 2018 • Chenyu You, Linfeng Yang, Yi Zhang, Ge Wang
The use of deep convolutional (Conv) neural networks for noise reduction in Low-Dose CT (LDCT) images has recently shown a great potential in this important application.
no code implementations • 16 Oct 2018 • Qing Lyu, Chenyu You, Hongming Shan, Ge Wang
Magnetic resonance imaging (MRI) is extensively used for diagnosis and image-guided therapeutics.
Medical Physics
no code implementations • 10 Aug 2018 • Chenyu You, Guang Li, Yi Zhang, Xiaoliu Zhang, Hongming Shan, Shenghong Ju, Zhen Zhao, Zhuiyang Zhang, Wenxiang Cong, Michael W. Vannier, Punam K. Saha, Ge Wang
Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.
no code implementations • 2 May 2018 • Chenyu You, Qingsong Yang, Hongming Shan, Lars Gjesteby, Guang Li, Shenghong Ju, Zhuiyang Zhang, Zhen Zhao, Yi Zhang, Wenxiang Cong, Ge Wang
However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality.