732 citations · 1.3k across the 22 of their papers we have counts for
23 papers
ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders
Shawn Xu, Lin Yang, Christopher Kelly +25
In this work, we present an approach, which we call Embeddings for Language/Image-aligned X-Rays, or ELIXR, that leverages a language-aligned image encoder combined or grafted onto…
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View
Di Jin, Elena Sergeeva, Wei-Hung Weng +2
The increasing availability of large collections of electronic health record (EHR) data and unprecedented technical advances in deep learning (DL) have sparked a surge of research…
Addressing the Real-world Class Imbalance Problem in Dermatology
Wei-Hung Weng, Jonathan Deaton, Vivek Natarajan +2
Class imbalance is a common problem in medical diagnosis, causing a standard classifier to be biased towards the common classes and perform poorly on the rare classes. This is espe…
What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
Di Jin, Eileen Pan, Nassim Oufattole +3
Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present t…
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output
Matthew B. A. McDermott, Tzu Ming Harry Hsu, Wei-Hung Weng +2
It is often infeasible or impossible to obtain ground truth labels for medical data. To circumvent this, one may build rule-based or other expert-knowledge driven labelers to inges…
Entity-Enriched Neural Models for Clinical Question Answering
Bhanu Pratap Singh Rawat, Wei-Hung Weng, So Yeon Min +2
We explore state-of-the-art neural models for question answering on electronic medical records and improve their ability to generalize better on previously unseen (paraphrased) que…
Weakly Supervised Context Encoder using DICOM metadata in Ultrasound Imaging
Szu-Yeu Hu, Shuhang Wang, Wei-Hung Weng +6
Modern deep learning algorithms geared towards clinical adaption rely on a significant amount of high fidelity labeled data. Low-resource settings pose challenges like acquiring hi…
Clinical Text Summarization with Syntax-Based Negation and Semantic Concept Identification
Wei-Hung Weng, Yu-An Chung, Schrasing Tong
In the era of clinical information explosion, a good strategy for clinical text summarization is helpful to improve the clinical workflow. The ideal summarization strategy can pres…
Human-centric Metric for Accelerating Pathology Reports Annotation
Ruibin Ma, Po-Hsuan Cameron Chen, Gang Li +4
Pathology reports contain useful information such as the main involved organ, diagnosis, etc. These information can be identified from the free text reports and used for large-scal…
Representation Learning for Electronic Health Records
Wei-Hung Weng, Peter Szolovits
Information in electronic health records (EHR), such as clinical narratives, examination reports, lab measurements, demographics, and other patient encounter entries, can be transf…
Machine Learning for Clinical Predictive Analytics
Wei-Hung Weng
In this chapter, we provide a brief overview of applying machine learning techniques for clinical prediction tasks. We begin with a quick introduction to the concepts of machine le…
Multimodal Multitask Representation Learning for Pathology Biobank Metadata Prediction
Wei-Hung Weng, Yuannan Cai, Angela Lin +2
Metadata are general characteristics of the data in a well-curated and condensed format, and have been proven to be useful for decision making, knowledge discovery, and also hetero…
Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery
Szu-Yeu Hu, Wei-Hung Weng, Shao-Lun Lu +4
Stereotactic radiosurgery (SRS), which delivers high doses of irradiation in a single or few shots to small targets, has been a standard of care for brain metastases. While very ef…
Publicly Available Clinical BERT Embeddings
Emily Alsentzer, John R. Murphy, Willie Boag +4
Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT (Devlin et al., 2018) have dramatically improved performance for many natural language processing (NLP)…
Clinically Accurate Chest X-Ray Report Generation
Guanxiong Liu, Tzu-Ming Harry Hsu, Matthew McDermott +4
The automatic generation of radiology reports given medical radiographs has significant potential to operationally and improve clinical patient care. A number of prior works have f…
Unsupervised Clinical Language Translation
Wei-Hung Weng, Yu-An Chung, Peter Szolovits
As patients' access to their doctors' clinical notes becomes common, translating professional, clinical jargon to layperson-understandable language is essential to improve patient-…
Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks for Clinical Interpretability
Uma M. Girkar, Ryo Uchimido, Li-wei H. Lehman +3
Determining whether hypotensive patients in intensive care units (ICUs) should receive fluid bolus therapy (FBT) has been an extremely challenging task for intensive care physician…
Unsupervised Multimodal Representation Learning across Medical Images and Reports
Tzu-Ming Harry Hsu, Wei-Hung Weng, Willie Boag +2
Joint embeddings between medical imaging modalities and associated radiology reports have the potential to offer significant benefits to the clinical community, ranging from cross-…
Towards Unsupervised Speech-to-Text Translation
Yu-An Chung, Wei-Hung Weng, Schrasing Tong +1
We present a framework for building speech-to-text translation (ST) systems using only monolingual speech and text corpora, in other words, speech utterances from a source language…
Mapping Unparalleled Clinical Professional and Consumer Languages with Embedding Alignment
Wei-Hung Weng, Peter Szolovits
Mapping and translating professional but arcane clinical jargons to consumer language is essential to improve the patient-clinician communication. Researchers have used the existin…
Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces
Yu-An Chung, Wei-Hung Weng, Schrasing Tong +1
Recent research has shown that word embedding spaces learned from text corpora of different languages can be aligned without any parallel data supervision. Inspired by the success…
Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval
Yu-An Chung, Wei-Hung Weng
Deep neural networks have been investigated in learning latent representations of medical images, yet most of the studies limit their approach in a single supervised convolutional…
Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients
Wei-Hung Weng, Mingwu Gao, Ze He +2
Glycemic control is essential for critical care. However, it is a challenging task because there has been no study on personalized optimal strategies for glycemic control. This wor…