Search Results for author: Yifan Peng

Found 49 papers, 19 papers with code

Automatic recognition of abdominal lymph nodes from clinical text

1 code implementation EMNLP (ClinicalNLP) 2020 Yifan Peng, SungWon Lee, Daniel C. Elton, Thomas Shen, Yu-Xing Tang, Qingyu Chen, Shuai Wang, Yingying Zhu, Ronald Summers, Zhiyong Lu

We then introduce an end-to-end approach based on the combination of rules and transformer-based methods to detect these abdominal lymph node mentions and classify their types from the MRI radiology reports.

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.

CMU’s IWSLT 2022 Dialect Speech Translation System

no code implementations IWSLT (ACL) 2022 Brian Yan, Patrick Fernandes, Siddharth Dalmia, Jiatong Shi, Yifan Peng, Dan Berrebbi, Xinyi Wang, Graham Neubig, Shinji Watanabe

We use additional paired Modern Standard Arabic data (MSA) to directly improve the speech recognition (ASR) and machine translation (MT) components of our cascaded systems.

Knowledge Distillation Machine Translation +2

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 +2

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.

CU-UD: text-mining drug and chemical-protein interactions with ensembles of BERT-based models

1 code implementation11 Nov 2021 Mehmet Efruz Karabulut, K. Vijay-Shanker, Yifan Peng

Our system obtained 0. 7708 in precision and 0. 7770 in recall, for an F1 score of 0. 7739, demonstrating the effectiveness of using ensembles of BERT-based language models for automatically detecting relations between chemicals and proteins.


Lymph Node Detection in T2 MRI with Transformers

no code implementations9 Nov 2021 Tejas Sudharshan Mathai, SungWon Lee, Daniel C. Elton, Thomas C. Shen, Yifan Peng, Zhiyong Lu, Ronald M. Summers

Identification of lymph nodes (LN) in T2 Magnetic Resonance Imaging (MRI) is an important step performed by radiologists during the assessment of lymphoproliferative diseases.

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.

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.

Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment

1 code implementation4 Sep 2021 Zhanghexuan Ji, Mohammad Abuzar Shaikh, Dana Moukheiber, Sargur Srihari, Yifan Peng, Mingchen Gao

Self-supervised learning provides an opportunity to explore unlabeled chest X-rays and their associated free-text reports accumulated in clinical routine without manual supervision.

Representation Learning Self-Supervised Learning

A framework for massive scale personalized promotion

no code implementations27 Aug 2021 Yitao Shen, Yue Wang, Xingyu Lu, Feng Qi, Jia Yan, Yixiang Mu, Yao Yang, Yifan Peng, Jinjie Gu

In order to do effective optimization in the second stage, counterfactual prediction and noise-reduction are essential for the first stage.

Improving BERT Model Using Contrastive Learning for Biomedical Relation Extraction

1 code implementation NAACL (BioNLP) 2021 Peng Su, Yifan Peng, K. Vijay-Shanker

In this work, we explore the method of employing contrastive learning to improve the text representation from the BERT model for relation extraction.

Contrastive Learning Data Augmentation +2

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

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

Deep-Learned Broadband Encoding Stochastic Filters for Computational Spectroscopic Instruments

no code implementations17 Dec 2020 Hongya Song, Yaoguang Ma, Yubing Han, Weidong Shen, Wenyi Zhang, Yanghui Li, Xu Liu, Yifan Peng, Xiang Hao

Computational spectroscopic instruments with Broadband Encoding Stochastic (BEST) filters allow the reconstruction of the spectrum at high precision with only a few filters.

Instrumentation and Detectors

Efficient Long-Range Convolutions for Point Clouds

1 code implementation11 Oct 2020 Yifan Peng, Lin Lin, Lexing Ying, Leonardo Zepeda-Núñez

We showcase this framework by introducing a neural network architecture that combines LRC-layers with short-range convolutional layers to accurately learn the energy and force associated with a $N$-body potential.

Navigating the landscape of COVID-19 research through literature analysis: A bird's eye view

no code implementations7 Aug 2020 Lana Yeganova, Rezarta Islamaj, Qingyu Chen, Robert Leaman, Alexis Allot, Chin-Hsuan Wei, Donald C. Comeau, Won Kim, Yifan Peng, W. John Wilbur, Zhiyong Lu

In this study we analyze the LitCovid collection, 13, 369 COVID-19 related articles found in PubMed as of May 15th, 2020 with the purpose of examining the landscape of literature and presenting it in a format that facilitates information navigation and understanding.

named-entity-recognition NER

COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature

1 code implementation11 Jun 2020 Yifan Peng, Yu-Xing Tang, Sung-Won Lee, Yingying Zhu, Ronald M. Summers, Zhiyong Lu

(1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved DL performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza and trained a DL baseline to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We trained an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR.

Anomaly Detection Computed Tomography (CT) +1

MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation

13 code implementations12 Aug 2019 Ke Yan, You-Bao Tang, Yifan Peng, Veit Sandfort, Mohammadhadi Bagheri, Zhiyong Lu, Ronald M. Summers

When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report.

Computed Tomography (CT) Lesion Detection +2

Deep Optics for Single-shot High-dynamic-range Imaging

no code implementations CVPR 2020 Christopher A. Metzler, Hayato Ikoma, Yifan Peng, Gordon Wetzstein

High-dynamic-range (HDR) imaging is crucial for many computer graphics and vision applications.

A deep learning approach for automated detection of geographic atrophy from color fundus photographs

1 code implementation7 Jun 2019 Tiarnan D. Keenan, Shazia Dharssi, Yifan Peng, Qingyu Chen, Elvira Agrón, Wai T. Wong, Zhiyong Lu, Emily Y. Chew

Results: The deep learning models (GA detection, CGA detection from all eyes, and centrality detection from GA eyes) had AUC of 0. 933-0. 976, 0. 939-0. 976, and 0. 827-0. 888, respectively.

A self-attention based deep learning method for lesion attribute detection from CT reports

no code implementations30 Apr 2019 Yifan Peng, Ke Yan, Veit Sandfort, Ronald M. Summers, Zhiyong Lu

In radiology, radiologists not only detect lesions from the medical image, but also describe them with various attributes such as their type, location, size, shape, and intensity.

Fine-grained lesion annotation in CT images with knowledge mined from radiology reports

no code implementations4 Mar 2019 Ke Yan, Yifan Peng, Zhiyong Lu, Ronald M. Summers

To address this problem, we define a set of 145 labels based on RadLex to describe a large variety of lesions in the DeepLesion dataset.

MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs

no code implementations21 Jan 2019 Alistair E. W. Johnson, Tom J. Pollard, Nathaniel R. Greenbaum, Matthew P. Lungren, Chih-ying Deng, Yifan Peng, Zhiyong Lu, Roger G. Mark, Seth J. Berkowitz, Steven Horng

Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient's thorax, but requiring specialized training for proper interpretation.

Natural Language Processing

DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs

1 code implementation19 Nov 2018 Yifan Peng, Shazia Dharssi, Qingyu Chen, Tiarnan D. Keenan, Elvira Agrón, Wai T. Wong, Emily Y. Chew, Zhiyong Lu

DeepSeeNet simulates the human grading process by first detecting individual AMD risk factors (drusen size, pigmentary abnormalities) for each eye and then calculating a patient-based AMD severity score using the AREDS Simplified Severity Scale.

Decision Making General Classification

ML-Net: multi-label classification of biomedical texts with deep neural networks

4 code implementations13 Nov 2018 Jingcheng Du, Qingyu Chen, Yifan Peng, Yang Xiang, Cui Tao, Zhiyong Lu

Due to this nature, the multi-label text classification task is often considered to be more challenging compared to the binary or multi-class text classification problems.

Classification Feature Engineering +6

BioSentVec: creating sentence embeddings for biomedical texts

4 code implementations22 Oct 2018 Qingyu Chen, Yifan Peng, Zhiyong Lu

Sentence embeddings have become an essential part of today's natural language processing (NLP) systems, especially together advanced deep learning methods.

 Ranked #1 on Sentence Embeddings For Biomedical Texts on MedSTS (using extra training data)

Natural Language Processing Sentence Embeddings For Biomedical Texts +1

Depth and Transient Imaging With Compressive SPAD Array Cameras

no code implementations CVPR 2018 Qilin Sun, Xiong Dun, Yifan Peng, Wolfgang Heidrich

Time-of-flight depth imaging and transient imaging are two imaging modalities that have recently received a lot of interest.

Compressive Sensing

Comment Generation for Source Code: State of the Art, Challenges and Opportunities

no code implementations5 Jan 2018 Xiaoran Wang, Yifan Peng, Benwen Zhang

One way to make software development more efficient is to make the program more readable.

Software Engineering

NegBio: a high-performance tool for negation and uncertainty detection in radiology reports

1 code implementation16 Dec 2017 Yifan Peng, Xiaosong Wang, Le Lu, Mohammadhadi Bagheri, Ronald Summers, Zhiyong Lu

Negative and uncertain medical findings are frequent in radiology reports, but discriminating them from positive findings remains challenging for information extraction.

Revisiting Cross-Channel Information Transfer for Chromatic Aberration Correction

no code implementations ICCV 2017 Tiancheng Sun, Yifan Peng, Wolfgang Heidrich

Image aberrations can cause severe degradation in image quality for consumer-level cameras, especially under the current tendency to reduce the complexity of lens designs in order to shrink the overall size of modules.

BioCreative VI Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations

no code implementations WS 2017 Rezarta Islamaj Do{\u{g}}an, Andrew Chatr-aryamontri, Sun Kim, Chih-Hsuan Wei, Yifan Peng, Donald Comeau, Zhiyong Lu

The Precision Medicine Track in BioCre-ative VI aims to bring together the Bi-oNLP community for a novel challenge focused on mining the biomedical litera-ture in search of mutations and protein-protein interactions (PPI).

Relation Extraction

Deep learning for extracting protein-protein interactions from biomedical literature

no code implementations WS 2017 Yifan Peng, Zhiyong Lu

State-of-the-art methods for protein-protein interaction (PPI) extraction are primarily feature-based or kernel-based by leveraging lexical and syntactic information.

Studying Relationships between Human Gaze, Description, and Computer Vision

no code implementations CVPR 2013 Kiwon Yun, Yifan Peng, Dimitris Samaras, Gregory J. Zelinsky, Tamara L. Berg

We posit that user behavior during natural viewing of images contains an abundance of information about the content of images as well as information related to user intent and user defined content importance.

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