139 citations · 765 across the 31 of their papers we have counts for
33 papers
When Prompt-based Incremental Learning Does Not Meet Strong Pretraining
Yu-Ming Tang, Yi-Xing Peng, Wei-Shi Zheng
Incremental learning aims to overcome catastrophic forgetting when learning deep networks from sequential tasks. With impressive learning efficiency and performance, prompt-based m…
From Military to Healthcare: Adopting and Expanding Ethical Principles for Generative Artificial Intelligence
David Oniani, Jordan Hilsman, Yifan Peng +5
In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despi…
Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension
Leilei Su, Jian Chen, Yifan Peng +1
Although deep learning techniques have shown significant achievements, they frequently depend on extensive amounts of hand-labeled data and tend to perform inadequately in few-shot…
A scoping review on multimodal deep learning in biomedical images and texts
Zhaoyi Sun, Mingquan Lin, Qingqing Zhu +4
Computer-assisted diagnostic and prognostic systems of the future should be capable of simultaneously processing multimodal data. Multimodal deep learning (MDL), which involves the…
Classifying Crime Types using Judgment Documents from Social Media
Haoxuan Xu, Zeyu He, Mengfan Shen +3
The task of determining crime types based on criminal behavior facts has become a very important and meaningful task in social science. But the problem facing the field now is that…
Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports
Qingqing Zhu, Tejas Sudharshan Mathai, Pritam Mukherjee +3
Despite the reduction in turn-around times in radiology reports with the use of speech recognition software, persistent communication errors can significantly impact the interpreta…
Less Likely Brainstorming: Using Language Models to Generate Alternative Hypotheses
Liyan Tang, Yifan Peng, Yanshan Wang +3
A human decision-maker benefits the most from an AI assistant that corrects for their biases. For problems such as generating interpretation of a radiology report given findings, a…
Generative Modeling via Hierarchical Tensor Sketching
Yifan Peng, Yian Chen, E. Miles Stoudenmire +1
We propose a hierarchical tensor-network approach for approximating high-dimensional probability density via empirical distribution. This leverages randomized singular value decomp…
FactReranker: Fact-guided Reranker for Faithful Radiology Report Summarization
Qianqian Xie, Jiayu Zhou, Yifan Peng +1
Automatic radiology report summarization is a crucial clinical task, whose key challenge is to maintain factual accuracy between produced summaries and ground truth radiology findi…
RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging
Ajay Jaiswal, Kumar Ashutosh, Justin F Rousseau +3
AI-powered Medical Imaging has recently achieved enormous attention due to its ability to provide fast-paced healthcare diagnoses. However, it usually suffers from a lack of high-q…
SODA: A Natural Language Processing Package to Extract Social Determinants of Health for Cancer Studies
Zehao Yu, Xi Yang, Chong Dang +12
Objective: We aim to develop an open-source natural language processing (NLP) package, SODA (i.e., SOcial DeterminAnts), with pre-trained transformer models to extract social deter…
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study
Gregory Holste, Song Wang, Ziyu Jiang +5
Imaging exams, such as chest radiography, will yield a small set of common findings and a much larger set of uncommon findings. While a trained radiologist can learn the visual pre…
Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data
Yu-Ming Tang, Yi-Xing Peng, Wei-Shi Zheng
Deep neural network (DNN) suffers from catastrophic forgetting when learning incrementally, which greatly limits its applications. Although maintaining a handful of samples (called…
Classifying Cyber-Risky Clinical Notes by Employing Natural Language Processing
Suzanna Schmeelk, Martins Samuel Dogo, Yifan Peng +1
Clinical notes, which can be embedded into electronic medical records, document patient care delivery and summarize interactions between healthcare providers and patients. These cl…
Radiology Text Analysis System (RadText): Architecture and Evaluation
Song Wang, Mingquan Lin, Ying Ding +3
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 o…
CU-UD: text-mining drug and chemical-protein interactions with ensembles of BERT-based models
Mehmet Efruz Karabulut, K. Vijay-Shanker, Yifan Peng
Identifying the relations between chemicals and proteins is an important text mining task. BioCreative VII track 1 DrugProt task aims to promote the development and evaluation of s…
Navigating the landscape of COVID-19 research through literature analysis: A bird's eye view
Lana Yeganova, Rezarta Islamaj, Qingyu Chen +8
Timely access to accurate scientific literature in the battle with the ongoing COVID-19 pandemic is critical. This unprecedented public health risk has motivated research towards u…
Predicting risk of late age-related macular degeneration using deep learning
Yifan Peng, Tiarnan D. Keenan, Qingyu Chen +5
By 2040, age-related macular degeneration (AMD) will affect approximately 288 million people worldwide. Identifying individuals at high risk of progression to late AMD, the sight-t…
COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature
Yifan Peng, Yu-Xing Tang, Sungwon Lee +3
The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collec…
An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining
Yifan Peng, Qingyu Chen, Zhiyong Lu
Multi-task learning (MTL) has achieved remarkable success in natural language processing applications. In this work, we study a multi-task learning model with multiple decoders on…
MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation
Ke Yan, Youbao Tang, Yifan Peng +4
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 describ…
Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets
Yifan Peng, Shankai Yan, Zhiyong Lu
Inspired by the success of the General Language Understanding Evaluation benchmark, we introduce the Biomedical Language Understanding Evaluation (BLUE) benchmark to facilitate res…
A deep learning approach for automated detection of geographic atrophy from color fundus photographs
Tiarnan D. Keenan, Shazia Dharssi, Yifan Peng +5
Purpose: To assess the utility of deep learning in the detection of geographic atrophy (GA) from color fundus photographs; secondary aim to explore potential utility in detecting c…
A self-attention based deep learning method for lesion attribute detection from CT reports
Yifan Peng, Ke Yan, Veit Sandfort +2
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.…