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.
1 code implementation • 8 May 2024 • Dawei Li, Shu Yang, Zhen Tan, Jae Young Baik, Sunkwon Yun, Joseph Lee, Aaron Chacko, BoJian Hou, Duy Duong-Tran, Ying Ding, Huan Liu, Li Shen, Tianlong Chen
With a synergized framework of LLM and KG mutually enhancing each other, we first leverage LLM to construct an evolving AD-specific knowledge graph (KG) sourced from AD-related scientific literature, and then we utilize a coarse-to-fine sampling method with a novel self-aware knowledge retrieval approach to select appropriate knowledge from the KG to augment LLM inference capabilities.
1 code implementation • 28 Mar 2024 • Song Wang, Yiliang Zhou, Ziqiang Han, Cui Tao, Yunyu Xiao, Ying Ding, Joydeep Ghosh, Yifan Peng
Data accuracy is essential for scientific research and policy development.
no code implementations • 11 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.
no code implementations • 25 Jan 2024 • Mingquan Lin, TianHao Li, Zhaoyi Sun, Gregory Holste, Ying Ding, Fei Wang, George Shih, Yifan Peng
Our proposed AI model utilizes supervised contrastive learning to minimize bias in CXR diagnosis.
1 code implementation • 17 Aug 2023 • Gregory Holste, Ziyu Jiang, Ajay Jaiswal, Maria Hanna, Shlomo Minkowitz, Alan C. Legasto, Joanna G. Escalon, Sharon Steinberger, Mark Bittman, Thomas C. Shen, Ying Ding, Ronald M. Summers, George Shih, Yifan Peng, Zhangyang Wang
This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification.
1 code implementation • 12 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.
no code implementations • 11 Jul 2023 • Chao Min, Yi Zhao, Yi Bu, Ying Ding, Caroline S. Wagner
Artificial Intelligence (AI), a cornerstone of 21st-century technology, has seen remarkable growth in China.
no code implementations • 20 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.
1 code implementation • 18 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.
1 code implementation • 18 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.
no code implementations • 30 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.
no code implementations • 18 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.
no code implementations • 27 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.
no code implementations • 16 Feb 2023 • Li Shi, Redoan Rahman, Esther Melamed, Jacek Gwizdka, Justin F. Rousseau, Ying Ding
This paper demonstrates the importance of XAI methods in cross-validation of feature attributions.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +2
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.
no code implementations • 6 Dec 2022 • Ajay Jaiswal, Tianlong Chen, Justin F. Rousseau, Yifan Peng, Ying Ding, Zhangyang Wang
However, DNNs are notoriously fragile to the class imbalance in image classification.
no code implementations • 15 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).
1 code implementation • 14 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.
1 code implementation • 10 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.
1 code implementation • 26 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.
1 code implementation • 19 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.
no code implementations • 17 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.
no code implementations • 11 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.
no code implementations • 28 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.
no code implementations • 27 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.
no code implementations • 29 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.
no code implementations • 9 Aug 2021 • Huimin Xu, Yi Bu, MeiJun Liu, Chenwei Zhang, Mengyi Sun, Yi Zhang, Eric Meyer, Eduardo Salas, Ying Ding
In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics.
no code implementations • 11 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.
no code implementations • 7 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.
no code implementations • 12 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.
no code implementations • 11 Jan 2021 • Tingyi Wanyan, Hossein Honarvar, Suraj K. Jaladanki, Chengxi Zang, Nidhi Naik, Sulaiman Somani, Jessica K. De Freitas, Ishan Paranjpe, Akhil Vaid, Riccardo Miotto, Girish N. Nadkarni, Marinka Zitnik, ArifulAzad, Fei Wang, Ying Ding, Benjamin S. Glicksberg
This has been a major issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, particularly within electronic health records (EHR) research.
1 code implementation • 8 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.
no code implementations • 28 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.
no code implementations • 25 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.
no code implementations • 18 Dec 2020 • Islam Akef Ebeid, Majdi Hassan, Tingyi Wanyan, Jack Roper, Abhik Seal, Ying Ding
Here we propose using the latest graph representation learning and embedding models to refine and complete biomedical knowledge graphs.
no code implementations • 25 Nov 2020 • Yan Han, Chongyan Chen, Liyan Tang, Mingquan Lin, Ajay Jaiswal, Song Wang, Ahmed Tewfik, George Shih, Ying Ding, Yifan Peng
After a number of iterations and with the help of radiomic features, our framework can converge to more accurate image regions.
no code implementations • 4 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.
1 code implementation • 21 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
no code implementations • 3 Apr 2020 • Tingyi Wanyan, Chenwei Zhang, Ariful Azad, Xiaomin Liang, Daifeng Li, Ying Ding
We present a multi-filtering Graph Convolution Neural Network (GCN) framework for network embedding task.
no code implementations • 27 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.
1 code implementation • 7 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.
no code implementations • 22 Jul 2018 • Chao Lu, Yi Bu, Jie Wang, Ying Ding, Vetle Torvik, Matthew Schnaars, Chengzhi Zhang
The observations suggest marginal differences between groups in syntactical and lexical complexity.
no code implementations • 6 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.
no code implementations • 20 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.