Search Results for author: Wei-Hung Weng

Found 30 papers, 11 papers with code

HeAR -- Health Acoustic Representations

no code implementations4 Mar 2024 Sebastien Baur, Zaid Nabulsi, Wei-Hung Weng, Jake Garrison, Louis Blankemeier, Sam Fishman, Christina Chen, Sujay Kakarmath, Minyoi Maimbolwa, Nsala Sanjase, Brian Shuma, Yossi Matias, Greg S. Corrado, Shwetak Patel, Shravya Shetty, Shruthi Prabhakara, Monde Muyoyeta, Diego Ardila

Health acoustic sounds such as coughs and breaths are known to contain useful health signals with significant potential for monitoring health and disease, yet are underexplored in the medical machine learning community.

Self-Supervised Learning

Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep Learning

no code implementations9 May 2023 Wei-Hung Weng, Sebastien Baur, Mayank Daswani, Christina Chen, Lauren Harrell, Sujay Kakarmath, Mariam Jabara, Babak Behsaz, Cory Y. McLean, Yossi Matias, Greg S. Corrado, Shravya Shetty, Shruthi Prabhakara, Yun Liu, Goodarz Danaei, Diego Ardila

We compared the DLS with the office-based refit-WHO score, which adopts the shared predictors from WHO and Globorisk scores (age, sex, smoking status, height, weight and systolic blood pressure) but refitted on the UK Biobank (UKB) cohort.

Photoplethysmography (PPG)

Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images

1 code implementation ICCV 2021 Richard J. Chen, Ming Y. Lu, Wei-Hung Weng, Tiffany Y. Chen, Drew F.K. Williamson, Trevor Manz, Maha Shady, Faisal Mahmood

Survival outcome prediction is a challenging weakly-supervised and ordinal regression task in computational pathology that involves modeling complex interactions within the tumor microenvironment in gigapixel whole slide images (WSIs).

Attribute Multiple Instance Learning +6

Addressing the Real-world Class Imbalance Problem in Dermatology

no code implementations9 Oct 2020 Wei-Hung Weng, Jonathan Deaton, Vivek Natarajan, Gamaleldin F. Elsayed, YuAn Liu

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.

Benchmarking Few-Shot Learning +1

What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

3 code implementations28 Sep 2020 Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, Peter Szolovits

Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community.

Multiple-choice Open-Domain Question Answering +1

CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output

1 code implementation26 Jun 2020 Matthew B. A. McDermott, Tzu Ming Harry Hsu, Wei-Hung Weng, Marzyeh Ghassemi, Peter Szolovits

CheXpert is very useful, but is relatively computationally slow, especially when integrated with end-to-end neural pipelines, is non-differentiable so can't be used in any applications that require gradients to flow through the labeler, and does not yield probabilistic outputs, which limits our ability to improve the quality of the silver labeler through techniques such as active learning.

Active Learning

Entity-Enriched Neural Models for Clinical Question Answering

2 code implementations WS 2020 Bhanu Pratap Singh Rawat, Wei-Hung Weng, So Yeon Min, Preethi Raghavan, Peter Szolovits

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) questions at test time.

Question Answering

Weakly Supervised Context Encoder using DICOM metadata in Ultrasound Imaging

no code implementations20 Mar 2020 Szu-Yeu Hu, Shuhang Wang, Wei-Hung Weng, JingChao Wang, XiaoHong Wang, Arinc Ozturk, Qian Li, Viksit Kumar, Anthony E. Samir

Modern deep learning algorithms geared towards clinical adaption rely on a significant amount of high fidelity labeled data.

Clinical Text Summarization with Syntax-Based Negation and Semantic Concept Identification

1 code implementation29 Feb 2020 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.

Negation Negation Detection +1

Machine Learning for Clinical Predictive Analytics

1 code implementation19 Sep 2019 Wei-Hung Weng

In this chapter, we provide a brief overview of applying machine learning techniques for clinical prediction tasks.

BIG-bench Machine Learning

Representation Learning for Electronic Health Records

no code implementations19 Sep 2019 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 transformed into appropriate data representations that can be used for downstream clinical machine learning tasks using representation learning.

BIG-bench Machine Learning Representation Learning

Multimodal Multitask Representation Learning for Pathology Biobank Metadata Prediction

1 code implementation17 Sep 2019 Wei-Hung Weng, Yuannan Cai, Angela Lin, Fraser Tan, Po-Hsuan Cameron Chen

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 heterogeneous data organization of biobank.

Decision Making Representation Learning

Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery

no code implementations15 Aug 2019 Szu-Yeu Hu, Wei-Hung Weng, Shao-Lun Lu, Yueh-Hung Cheng, Furen Xiao, Feng-Ming Hsu, Jen-Tang Lu

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.

Publicly Available Clinical BERT Embeddings

2 code implementations WS 2019 Emily Alsentzer, John R. Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, Matthew B. A. McDermott

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) tasks in recent months.


Clinically Accurate Chest X-Ray Report Generation

1 code implementation4 Apr 2019 Guanxiong Liu, Tzu-Ming Harry Hsu, Matthew McDermott, Willie Boag, Wei-Hung Weng, Peter Szolovits, Marzyeh Ghassemi

The automatic generation of radiology reports given medical radiographs has significant potential to operationally and improve clinical patient care.

Text Generation

Unsupervised Clinical Language Translation

1 code implementation4 Feb 2019 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-clinician communication.

Clinical Language Translation Representation Learning +3

Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks for Clinical Interpretability

no code implementations3 Dec 2018 Uma M. Girkar, Ryo Uchimido, Li-wei H. Lehman, Peter Szolovits, Leo Celi, Wei-Hung Weng

Determining whether hypotensive patients in intensive care units (ICUs) should receive fluid bolus therapy (FBT) has been an extremely challenging task for intensive care physicians as the corresponding increase in blood pressure has been hard to predict.

regression Time Series +1

Unsupervised Multimodal Representation Learning across Medical Images and Reports

no code implementations21 Nov 2018 Tzu-Ming Harry Hsu, Wei-Hung Weng, Willie Boag, Matthew McDermott, Peter Szolovits

Joint embeddings between medical imaging modalities and associated radiology reports have the potential to offer significant benefits to the clinical community, ranging from cross-domain retrieval to conditional generation of reports to the broader goals of multimodal representation learning.

Representation Learning Retrieval

Towards Unsupervised Speech-to-Text Translation

no code implementations4 Nov 2018 Yu-An Chung, Wei-Hung Weng, Schrasing Tong, James Glass

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 and independent text from a target language.

Denoising Language Modelling +3

Mapping Unparalleled Clinical Professional and Consumer Languages with Embedding Alignment

no code implementations25 Jun 2018 Wei-Hung Weng, Peter Szolovits

In this work, we utilized the embeddings alignment method for the word mapping between unparalleled clinical professional and consumer language embeddings.

Retrieval Word Embeddings

Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces

no code implementations NeurIPS 2018 Yu-An Chung, Wei-Hung Weng, Schrasing Tong, James Glass

Recent research has shown that word embedding spaces learned from text corpora of different languages can be aligned without any parallel data supervision.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +5

Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients

no code implementations2 Dec 2017 Wei-Hung Weng, Mingwu Gao, Ze He, Susu Yan, Peter Szolovits

This work aims to learn personalized optimal glycemic trajectories for severely ill septic patients by learning data-driven policies to identify optimal targeted blood glucose levels as a reference for clinicians.

reinforcement-learning Reinforcement Learning (RL)

Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval

no code implementations22 Nov 2017 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 neural network (CNN), which usually rely heavily on a large scale annotated dataset for training.

Content-Based Image Retrieval Medical Image Retrieval +1

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