Search Results for author: Li Lin

Found 40 papers, 19 papers with code

SimpleNLG-TI: Adapting SimpleNLG to Tibetan

no code implementations INLG (ACL) 2020 Zewang Kuanzhuo, Li Lin, Zhao Weina

Surface realisation is the last but not the least phase of Natural Language Generation, which aims to produce high-quality natural language text based on meaning representations.

Text Generation

FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation

no code implementations27 Feb 2024 Li Lin, Yixiang Liu, Jiewei Wu, Pujin Cheng, Zhiyuan Cai, Kenneth K. Y. Wong, Xiaoying Tang

In such context, we propose a novel personalized FL framework with learnable prompt and aggregation (FedLPPA) to uniformly leverage heterogeneous weak supervision for medical image segmentation.

Federated Learning Image Segmentation +4

Detecting Multimedia Generated by Large AI Models: A Survey

1 code implementation22 Jan 2024 Li Lin, Neeraj Gupta, Yue Zhang, Hainan Ren, Chun-Hao Liu, Feng Ding, Xin Wang, Xin Li, Luisa Verdoliva, Shu Hu

The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and large language models, has marked a new era where AI-generated multimedia is increasingly integrated into various aspects of daily life.

Quantum Probability Theoretic Asset Return Modeling: A Novel Schrödinger-Like Trading Equation and Multimodal Distribution

no code implementations11 Jan 2024 Li Lin

The complex phase of quantum probability, capturing transitions between long and short decisions while considering information interaction among traders, offers an inherent advantage over classical probability in characterizing the multimodal distribution of asset returns. Utilizing Fourier decomposition, we derive a Schr\"odinger-like trading equation, where each term explicitly corresponds to implications of market trading.

ZONE: Zero-Shot Instruction-Guided Local Editing

no code implementations28 Dec 2023 Shanglin Li, Bohan Zeng, Yutang Feng, Sicheng Gao, Xuhui Liu, Jiaming Liu, Li Lin, Xu Tang, Yao Hu, Jianzhuang Liu, Baochang Zhang

We then propose a Region-IoU scheme for precise image layer extraction from an off-the-shelf segment model.

Image Generation

ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation

no code implementations13 Dec 2023 Shiyun Chen, Li Lin, Pujin Cheng, Xiaoying Tang

Recently, Segment Anything Model (SAM) has shown promising performance in some medical image segmentation tasks, but it performs poorly for liver tumor segmentation.

General Knowledge Image Segmentation +3

Super-Resolution on Rotationally Scanned Photoacoustic Microscopy Images Incorporating Scanning Prior

1 code implementation12 Dec 2023 Kai Pan, Linyang Li, Li Lin, Pujin Cheng, Junyan Lyu, Lei Xi, Xiaoyin Tang

Recently, there is a trend to incorporate deep learning into the scanning process to further increase the scanning speed. Yet, most such attempts are performed for raster scanning while those for rotational scanning are relatively rare.

Super-Resolution

PRIOR: Prototype Representation Joint Learning from Medical Images and Reports

1 code implementation ICCV 2023 Pujin Cheng, Li Lin, Junyan Lyu, Yijin Huang, Wenhan Luo, Xiaoying Tang

In this paper, we present a prototype representation learning framework incorporating both global and local alignment between medical images and reports.

Contrastive Learning Image-to-Text Retrieval +8

JOINEDTrans: Prior Guided Multi-task Transformer for Joint Optic Disc/Cup Segmentation and Fovea Detection

no code implementations19 May 2023 Huaqing He, Li Lin, Zhiyuan Cai, Pujin Cheng, Xiaoying Tang

To address these issues, we propose a prior guided multi-task transformer framework for joint OD/OC segmentation and fovea detection, named JOINEDTrans.

Fovea Detection Image Segmentation +2

Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation

1 code implementation12 Apr 2023 Li Lin, Jiewei Wu, Yixiang Liu, Kenneth K. Y. Wong, Xiaoying Tang

The statistical heterogeneity (e. g., non-IID data and domain shifts) is a primary obstacle in FL, impairing the generalization performance of the global model.

Federated Learning Image Segmentation +4

Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement

1 code implementation8 Mar 2023 Puijin Cheng, Li Lin, Yijin Huang, Huaqing He, Wenhan Luo, Xiaoying Tang

In this paper, we introduce a novel diffusion model based framework, named Learning Enhancement from Degradation (LED), for enhancing fundus images.

Image Enhancement

The inverse Cox-Ingersoll-Ross process for parsimonious financial price modeling

no code implementations22 Feb 2023 Li Lin, Didier Sornette

We propose a formulation to construct new classes of financial price processes based on the insight that the key variable driving prices $P$ is the earning-over-price ratio $\gamma \simeq 1/P$, which we refer to as the earning yield and is analogous to the yield-to-maturity of an equivalent perpetual bond.

YoloCurvSeg: You Only Label One Noisy Skeleton for Vessel-style Curvilinear Structure Segmentation

1 code implementation11 Dec 2022 Li Lin, Linkai Peng, Huaqing He, Pujin Cheng, Jiewei Wu, Kenneth K. Y. Wong, Xiaoying Tang

With only one noisy skeleton annotation (respectively 0. 14\%, 0. 03\%, 1. 40\%, and 0. 65\% of the full annotation), YoloCurvSeg achieves more than 97\% of the fully-supervised performance on each dataset.

Contrastive Learning Image Generation +4

Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph

1 code implementation8 Aug 2022 Aiwei Liu, Xuming Hu, Li Lin, Lijie Wen

First, we extract a schema linking graph from PLMs through a probing procedure in an unsupervised manner.

Graph Learning SQL Parsing +1

AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation

1 code implementation27 Jul 2022 Junyan Lyu, Yiqi Zhang, Yijin Huang, Li Lin, Pujin Cheng, Xiaoying Tang

To address this issue, we propose a data manipulation based domain generalization method, called Automated Augmentation for Domain Generalization (AADG).

Data Augmentation Domain Generalization +5

A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference

no code implementations31 May 2022 Shu'ang Li, Xuming Hu, Li Lin, Aiwei Liu, Lijie Wen, Philip S. Yu

Natural Language Inference (NLI) is a growingly essential task in natural language understanding, which requires inferring the relationship between the sentence pairs (premise and hypothesis).

Contrastive Learning Data Augmentation +5

DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Network

no code implementations14 Mar 2022 Ziqi Huang, Li Lin, Pujin Cheng, Kai Pan, Xiaoying Tang

Furthermore, with only 5% paired data, the proposed DS3-Net achieves competitive performance with state-of-theart image translation methods utilizing 100% paired data, delivering an average SSIM of 0. 8947 and an average PSNR of 23. 60.

Knowledge Distillation SSIM +1

Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked Image Modeling Transformer for Ophthalmic Image Classification

no code implementations9 Mar 2022 Zhiyuan Cai, Li Lin, Huaqing He, Xiaoying Tang

We employ a Unified Patch Embedding module to replace the origin patch embedding module in ViT for jointly processing both 2D and 3D input images.

Image Classification Self-Supervised Learning

Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion

no code implementations9 Mar 2022 Ziqi Huang, Li Lin, Pujin Cheng, Linkai Peng, Xiaoying Tang

As such, it is clinically meaningful to develop a method to synthesize unavailable modalities which can also be used as additional inputs to downstream tasks (e. g., brain tumor segmentation) for performance enhancing.

Brain Tumor Segmentation Contrastive Learning +2

JOINED : Prior Guided Multi-task Learning for Joint Optic Disc/Cup Segmentation and Fovea Detection

1 code implementation1 Mar 2022 Huaqing He, Li Lin, Zhiyuan Cai, Xiaoying Tang

At the coarse stage, we obtain the OD/OC coarse segmentation and the heatmap localization of fovea through a joint segmentation and detection module.

Fovea Detection Multi-Task Learning +1

GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges

no code implementations14 Feb 2022 Junde Wu, Huihui Fang, Fei Li, Huazhu Fu, Fengbin Lin, Jiongcheng Li, Lexing Huang, Qinji Yu, Sifan Song, Xinxing Xu, Yanyu Xu, Wensai Wang, Lingxiao Wang, Shuai Lu, Huiqi Li, Shihua Huang, Zhichao Lu, Chubin Ou, Xifei Wei, Bingyuan Liu, Riadh Kobbi, Xiaoying Tang, Li Lin, Qiang Zhou, Qiang Hu, Hrvoje Bogunovic, José Ignacio Orlando, Xiulan Zhang, Yanwu Xu

However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.

What Makes the Story Forward? Inferring Commonsense Explanations as Prompts for Future Event Generation

no code implementations18 Jan 2022 Li Lin, Yixin Cao, Lifu Huang, Shu'ang Li, Xuming Hu, Lijie Wen, Jianmin Wang

To alleviate the knowledge forgetting issue, we design two modules, Im and Gm, for each type of knowledge, which are combined via prompt tuning.

Information Retrieval Retrieval +1

Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning

1 code implementation13 Jan 2022 Linkai Peng, Li Lin, Pujin Cheng, Ziqi Huang, Xiaoying Tang

The two models use labeled data (together with the corresponding transferred images) for supervised learning and perform collaborative consistency learning on unlabeled data.

Image Reconstruction Retinal Vessel Segmentation +3

COROLLA: An Efficient Multi-Modality Fusion Framework with Supervised Contrastive Learning for Glaucoma Grading

1 code implementation11 Jan 2022 Zhiyuan Cai, Li Lin, Huaqing He, Xiaoying Tang

In this paper, we propose an efficient multi-modality supervised contrastive learning framework, named COROLLA, for glaucoma grading.

Contrastive Learning

Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation

2 code implementations27 Oct 2021 Yijin Huang, Li Lin, Pujin Cheng, Junyan Lyu, Roger Tam, Xiaoying Tang

To identify the key components in a standard deep learning framework (ResNet-50) for DR grading, we systematically analyze the impact of several major components.

Data Augmentation Diabetic Retinopathy Grading

Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction

1 code implementation EMNLP 2021 Xuming Hu, Chenwei Zhang, Yawen Yang, Xiaohe Li, Li Lin, Lijie Wen, Philip S. Yu

Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce.

Meta-Learning Pseudo Label +5

Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images

2 code implementations17 Jul 2021 Yijin Huang, Li Lin, Pujin Cheng, Junyan Lyu, Xiaoying Tang

Instead of taking entire images as the input in the common contrastive learning scheme, lesion patches are employed to encourage the feature extractor to learn representations that are highly discriminative for DR grading.

Contrastive Learning Data Augmentation +1

BSDA-Net: A Boundary Shape and Distance Aware Joint Learning Framework for Segmenting and Classifying OCTA Images

1 code implementation10 Jul 2021 Li Lin, Zhonghua Wang, Jiewei Wu, Yijin Huang, Junyan Lyu, Pujin Cheng, Jiong Wu, Xiaoying Tang

Moreover, both low-level and high-level features from the aforementioned three branches, including shape, size, boundary, and signed directional distance map of FAZ, are fused hierarchically with features from the diagnostic classifier.

Classification Segmentation

A New Physically Triggered Cell Death via Transbarrier Contactless Cold Atmospheric Plasma Treatment of Cancer Cells

no code implementations30 Mar 2020 Dayun Yan, Qihui Wang, Manish Adhikari, Alisa Malyavko, Li Lin, Denis B. Zolotukhin, Xiaoliang Yao, Megan Kirschner, Jonathan H. Sherman, Michael Keidar

The physical-triggered growth inhibition is due to a new type of cell death, characterized by rapid leakage of bulk water from the cells, resulting in bubbles on the cell membrane, and cytoplasm shrinkage.

An External Knowledge Enhanced Multi-label Charge Prediction Approach with Label Number Learning

no code implementations4 Jul 2019 Duan Wei, Li Lin

It combines the output probabilities of samples and their corresponding label numbers to get final prediction results.

An Approach for Process Model Extraction By Multi-Grained Text Classification

1 code implementation16 May 2019 Chen Qian, Lijie Wen, Akhil Kumar, Leilei Lin, Li Lin, Zan Zong, Shuang Li, Jian-Min Wang

Process model extraction (PME) is a recently emerged interdiscipline between natural language processing (NLP) and business process management (BPM), which aims to extract process models from textual descriptions.

General Classification Management +5

Remaining useful life estimation of engineered systems using vanilla LSTM neural networks

no code implementations Neurocomputing 2018 Yuting Wu, Mei Yuan, Shaopeng Dong, Li Lin, Yingqi Liu b

Following that, this paper aims to propose utilizing vanilla LSTM neural networks to get good RUL prediction accuracy which makes the most of long short-term memory ability, in the cases of complicated operations, working conditions, model degradations and strong noises.

Management

G-Bean: an ontology-graph based web tool for biomedical literature retrieval

no code implementations31 Aug 2015 Wang James Z., Zhang Yuanyuan, Dong Liang, Li Lin, Srimani Pradip K, Yu Philip S.

To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently. G-Bean addresses PubMed's limitations with three innovations: parallel document index creation, ontology-graph based query expansion, and retrieval and re-ranking of documents based on user's search intention. Performance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database.

Re-Ranking Retrieval

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