Search Results for author: Ying Ding

Found 47 papers, 13 papers with code

EchoGen: Generating Conclusions from Echocardiogram Notes

no code implementations BioNLP (ACL) 2022 Liyan Tang, Shravan Kooragayalu, Yanshan Wang, Ying Ding, Greg Durrett, Justin F. Rousseau, Yifan Peng

Generating a summary from findings has been recently explored (Zhang et al., 2018, 2020) in note types such as radiology reports that typically have short length.


Thought Graph: Generating Thought Process for Biological Reasoning

no code implementations11 Mar 2024 Chi-Yang Hsu, Kyle Cox, Jiawei Xu, Zhen Tan, Tianhua Zhai, Mengzhou Hu, Dexter Pratt, Tianlong Chen, Ziniu Hu, Ying Ding

We present the Thought Graph as a novel framework to support complex reasoning and use gene set analysis as an example to uncover semantic relationships between biological processes.

tdCoxSNN: Time-Dependent Cox Survival Neural Network for Continuous-time Dynamic Prediction

1 code implementation12 Jul 2023 Lang Zeng, Jipeng Zhang, Wei Chen, Ying Ding

In pursuit of constructing a dynamic prediction model for a progressive eye disorder, age-related macular degeneration (AMD), we propose a time-dependent Cox survival neural network (tdCoxSNN) to predict its progression using longitudinal fundus images.

An empirical study of using radiology reports and images to improve ICU mortality prediction

no code implementations20 Jun 2023 Mingquan Lin, Song Wang, Ying Ding, Lihui Zhao, Fei Wang, Yifan Peng

Background: The predictive Intensive Care Unit (ICU) scoring system plays an important role in ICU management because it predicts important outcomes, especially mortality.

ICU Mortality Management +1

Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication

1 code implementation18 Jun 2023 Ajay Jaiswal, Shiwei Liu, Tianlong Chen, Ying Ding, Zhangyang Wang

By dividing giant graph data, we build multiple independently and parallelly trained weaker GNNs (soup ingredient) without any intermediate communication, and combine their strength using a greedy interpolation soup procedure to achieve state-of-the-art performance.

graph partitioning Graph Sampling

Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models

1 code implementation18 Jun 2023 Ajay Jaiswal, Shiwei Liu, Tianlong Chen, Ying Ding, Zhangyang Wang

Motivated by the recent observations of model soups, which suggest that fine-tuned weights of multiple models can be merged to a better minima, we propose Instant Soup Pruning (ISP) to generate lottery ticket quality subnetworks, using a fraction of the original IMP cost by replacing the expensive intermediate pruning stages of IMP with computationally efficient weak mask generation and aggregation routine.

Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses

no code implementations30 May 2023 Liyan Tang, Yifan Peng, Yanshan Wang, Ying Ding, Greg Durrett, Justin F. Rousseau

To tackle this problem, we propose a controlled text generation method that uses a novel contrastive learning strategy to encourage models to differentiate between generating likely and less likely outputs according to humans.

Contrastive Learning Decision Making +1

CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models

no code implementations18 Apr 2023 TianHao Li, Sandesh Shetty, Advaith Kamath, Ajay Jaiswal, Xianqian Jiang, Ying Ding, Yejin Kim

Large pre-trained language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data.

Few-Shot Learning

Analyzing Impact of Socio-Economic Factors on COVID-19 Mortality Prediction Using SHAP Value

no code implementations27 Feb 2023 Redoan Rahman, Jooyeong Kang, Justin F Rousseau, Ying Ding

This paper applies multiple machine learning (ML) algorithms to a dataset of de-identified COVID-19 patients provided by the COVID-19 Research Database.

Mortality Prediction

Vision HGNN: An Image is More than a Graph of Nodes

1 code implementation ICCV 2023 Yan Han, Peihao Wang, Souvik Kundu, Ying Ding, Zhangyang Wang

In this paper, we enhance ViG by transcending conventional "pairwise" linkages and harnessing the power of the hypergraph to encapsulate image information.

graph construction Image Classification +2

RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging

no code implementations15 Oct 2022 Ajay Jaiswal, Kumar Ashutosh, Justin F Rousseau, Yifan Peng, Zhangyang Wang, Ying Ding

Our extensive experiments on popular medical imaging classification tasks (cardiopulmonary disease and lesion classification) using real-world datasets, show the performance benefit of RoS-KD, its ability to distill knowledge from many popular large networks (ResNet-50, DenseNet-121, MobileNet-V2) in a comparatively small network, and its robustness to adversarial attacks (PGD, FSGM).

Classification Knowledge Distillation +1

Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again

1 code implementation14 Oct 2022 Ajay Jaiswal, Peihao Wang, Tianlong Chen, Justin F. Rousseau, Ying Ding, Zhangyang Wang

In this paper, firstly, we provide a new perspective of gradient flow to understand the substandard performance of deep GCNs and hypothesize that by facilitating healthy gradient flow, we can significantly improve their trainability, as well as achieve state-of-the-art (SOTA) level performance from vanilla-GCNs.

Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays

1 code implementation10 Jul 2022 Yan Han, Gregory Holste, Ying Ding, Ahmed Tewfik, Yifan Peng, Zhangyang Wang

Using the learned self-attention of its image branch, RGT extracts a bounding box for which to compute radiomic features, which are further processed by the radiomics branch; learned image and radiomic features are then fused and mutually interact via cross-attention layers.

Training Your Sparse Neural Network Better with Any Mask

1 code implementation26 Jun 2022 Ajay Jaiswal, Haoyu Ma, Tianlong Chen, Ying Ding, Zhangyang Wang

Pruning large neural networks to create high-quality, independently trainable sparse masks, which can maintain similar performance to their dense counterparts, is very desirable due to the reduced space and time complexity.

Radiology Text Analysis System (RadText): Architecture and Evaluation

1 code implementation19 Mar 2022 Song Wang, Mingquan Lin, Ying Ding, George Shih, Zhiyong Lu, Yifan Peng

Analyzing radiology reports is a time-consuming and error-prone task, which raises the need for an efficient automated radiology report analysis system to alleviate the workloads of radiologists and encourage precise diagnosis.

De-identification named-entity-recognition +5

The Gene of Scientific Success

no code implementations17 Feb 2022 Xiangjie Kong, Jun Zhang, Da Zhang, Yi Bu, Ying Ding, Feng Xia

Under this consideration, our paper presents and analyzes the causal factors that are crucial for scholars' academic success.

Prior Knowledge Enhances Radiology Report Generation

no code implementations11 Jan 2022 Song Wang, Liyan Tang, Mingquan Lin, George Shih, Ying Ding, Yifan Peng

In this work, we propose to mine and represent the associations among medical findings in an informative knowledge graph and incorporate this prior knowledge with radiology report generation to help improve the quality of generated reports.

RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classification

no code implementations28 Oct 2021 Ajay Jaiswal, Liyan Tang, Meheli Ghosh, Justin Rousseau, Yifan Peng, Ying Ding

Radiology reports are unstructured and contain the imaging findings and corresponding diagnoses transcribed by radiologists which include clinical facts and negated and/or uncertain statements.

Classification Contrastive Learning

SCALP -- Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata

no code implementations27 Oct 2021 Ajay Jaiswal, TianHao Li, Cyprian Zander, Yan Han, Justin F. Rousseau, Yifan Peng, Ying Ding

In this paper, we proposed a novel and simple data augmentation method based on patient metadata and supervised knowledge to create clinically accurate positive and negative augmentations for chest X-rays.

Contrastive Learning Data Augmentation

CheXT: Knowledge-Guided Cross-Attention Transformer for Abnormality Classification and Localization in Chest X-rays

no code implementations29 Sep 2021 Yan Han, Ying Ding, Ahmed Tewfik, Yifan Peng, Zhangyang Wang

During training, the image branch leverages its learned attention to estimate pathology localization, which is then utilized to extract radiomic features from images in the radiomics branch.

Knowledge-Augmented Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop

no code implementations11 Apr 2021 Yan Han, Chongyan Chen, Ahmed Tewfik, Benjamin Glicksberg, Ying Ding, Yifan Peng, Zhangyang Wang

The key knob of our framework is a unique positive sampling approach tailored for the medical images, by seamlessly integrating radiomic features as a knowledge augmentation.

Contrastive Learning

Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data

no code implementations7 Apr 2021 Tingyi Wanyan, Jing Zhang, Ying Ding, Ariful Azad, Zhangyang Wang, Benjamin S Glicksberg

Electronic Health Record (EHR) data has been of tremendous utility in Artificial Intelligence (AI) for healthcare such as predicting future clinical events.

Attribute Contrastive Learning +1

Pneumonia Detection on Chest X-ray using Radiomic Features and Contrastive Learning

no code implementations12 Jan 2021 Yan Han, Chongyan Chen, Ahmed H Tewfik, Ying Ding, Yifan Peng

Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era.

Contrastive Learning Pneumonia Detection

Spending Your Winning Lottery Better After Drawing It

1 code implementation8 Jan 2021 Ajay Kumar Jaiswal, Haoyu Ma, Tianlong Chen, Ying Ding, Zhangyang Wang

In this paper, we demonstrate that it is unnecessary for spare retraining to strictly inherit those properties from the dense network.

Knowledge Distillation

Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records

no code implementations28 Dec 2020 Tingyi Wanyan, Hossein Honarvar, Ariful Azad, Ying Ding, Benjamin S. Glicksberg

In this work, we train a Heterogeneous Graph Model (HGM) on Electronic Health Record data and use the resulting embedding vector as additional information added to a Convolutional Neural Network (CNN) model for predicting in-hospital mortality.

Mortality Prediction

Understanding Team Collaboration in Artificial Intelligence from the perspective of Geographic Distance

no code implementations25 Dec 2020 Xuli Tang, Xin Li, Ying Ding, Feicheng Ma

This paper analyzes team collaboration in the field of Artificial Intelligence (AI) from the perspective of geographic distance.

Coronavirus Knowledge Graph: A Case Study

no code implementations4 Jul 2020 Chongyan Chen, Islam Akef Ebeid, Yi Bu, Ying Ding

The emergence of the novel COVID-19 pandemic has had a significant impact on global healthcare and the economy over the past few months.

graph construction Knowledge Graphs

Analysis of misinformation during the COVID-19 outbreak in China: cultural, social and political entanglements

1 code implementation21 May 2020 Yan Leng, Yujia Zhai, Shaojing Sun, Yifei Wu, Jordan Selzer, Sharon Strover, Julia Fensel, Alex Pentland, Ying Ding

COVID-19 resulted in an infodemic, which could erode public trust, impede virus containment, and outlive the pandemic itself.

Social and Information Networks Computers and Society

Analyzing Linguistic Complexity and Scientific Impact

no code implementations27 Jul 2019 Chao Lu, Yi Bu, Xianlei Dong, Jie Wang, Ying Ding, Vincent Larivière, Cassidy R. Sugimoto, Logan Paul, Chengzhi Zhang

In this context, scientific writing increasingly plays an important role in scholars' scientific careers.

edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

1 code implementation7 Sep 2018 Zheng Gao, Gang Fu, Chunping Ouyang, Satoshi Tsutsui, Xiaozhong Liu, Jeremy Yang, Christopher Gessner, Brian Foote, David Wild, Qi Yu, Ying Ding

We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.

Biomedical Information Retrieval Information Retrieval +3

DIMM-SC: A Dirichlet mixture model for clustering droplet-based single cell transcriptomic data

no code implementations6 Apr 2017 Zhe Sun, Ting Wang, Ke Deng, Xiao-Feng Wang, Robert Lafyatis, Ying Ding, Ming Hu, Wei Chen

More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods.


Meta Path-Based Collective Classification in Heterogeneous Information Networks

no code implementations20 May 2013 Xiangnan Kong, Bokai Cao, Philip S. Yu, Ying Ding, David J. Wild

Moreover, by considering different linkage paths in the network, one can capture the subtlety of different types of dependencies among objects.

Classification General Classification

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