Search Results for author: Chenyu You

Found 40 papers, 8 papers with code

Large Language Models Are Partially Primed in Pronoun Interpretation

1 code implementation26 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.

MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation

no code implementations8 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.

Image Segmentation Medical Image Segmentation +1

Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts

no code implementations6 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.

Image Segmentation Medical Image Segmentation +2

Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation

no code implementations6 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.

Contrastive Learning Image Segmentation +4

ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast

1 code implementation5 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.

Contrastive Learning Image Segmentation +2

Bridge the Gap between Language models and Tabular Understanding

no code implementations16 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.

Contrastive Learning Language Modelling +2

Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective

1 code implementation3 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.

Contrastive Learning Image Segmentation +2

Aligning Source Visual and Target Language Domains for Unpaired Video Captioning

no code implementations22 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.

Translation Video Captioning

Generating Accurate and Faithful Discharge Instructions: Task, Dataset, and Model

2 code implementations23 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.

Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels

no code implementations27 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.

Anatomy Contrastive Learning +3

Learning correspondences of cardiac motion from images using biomechanics-informed modeling

1 code implementation1 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.

Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension

no code implementations18 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.

Machine Reading Comprehension

Multi-scale Super-resolution Magnetic Resonance Spectroscopic Imaging with Adjustable Sharpness

no code implementations17 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.


Graph-in-Graph Network for Automatic Gene Ontology Description Generation

no code implementations10 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.

Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation

no code implementations3 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.

Image Segmentation Incremental Learning +3

End-to-end Spoken Conversational Question Answering: Task, Dataset and Model

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.

Conversational Question Answering Spoken Language Understanding +1

Diffeomorphic Image Registration with Neural Velocity Field

no code implementations25 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.

Image Registration

KerGNNs: Interpretable Graph Neural Networks with Graph Kernels

1 code implementation3 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.

Graph Classification

MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution

no code implementations28 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.

Graph Attention Space-time Video Super-resolution +1

Stingy Teacher: Sparse Logits Suffice to Fail Knowledge Distillation

no code implementations29 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.

Knowledge Distillation

Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering

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.

Question Answering Representation Learning

SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation

no code implementations13 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.

Data Augmentation Image Generation +4

Undistillable: Making A Nasty Teacher That CANNOT teach students

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.

Knowledge Distillation

Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation

no code implementations14 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.

Contrastive Learning Image Segmentation +4

Adaptive Bi-directional Attention: Exploring Multi-Granularity Representations for Machine Reading Comprehension

no code implementations20 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.

Machine Reading Comprehension

Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering

no code implementations21 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

Knowledge Distillation for Improved Accuracy in Spoken Question Answering

no code implementations21 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

Towards Data Distillation for End-to-end Spoken Conversational Question Answering

no code implementations18 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

Rethinking and Improving Natural Language Generation with Layer-Wise Multi-View Decoding

no code implementations16 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.

Abstractive Text Summarization Image Captioning +5

Low-Dose CT via Deep CNN with Skip Connection and Network in Network

no code implementations26 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.

Computed Tomography (CT)

Super-resolution MRI through Deep Learning

no code implementations16 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

CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)

no code implementations10 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.

Computed Tomography (CT) Image Restoration +1

Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising

no code implementations2 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.

Computed Tomography (CT) Denoising

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