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Found 28 papers, 11 papers with code
HeAR -- Health Acoustic Representations
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.
Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals
A crucial aspect of optimizing Slowfast NFNet for this application lies in identifying effective audio augmentations.
ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders
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 a fixed LLM, PaLM 2, to perform a broad range of chest X-ray tasks.
Predicting Cardiovascular Disease Risk using Photoplethysmography and Deep Learning
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.
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View
In this review, we focus on the interpretability of the DL models in healthcare.
Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images
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).
Addressing the Real-world Class Imbalance Problem in Dermatology
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.
What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community.
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output
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.
Entity-Enriched Neural Models for Clinical Question Answering
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.
Weakly Supervised Context Encoder using DICOM metadata in Ultrasound Imaging
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
In the era of clinical information explosion, a good strategy for clinical text summarization is helpful to improve the clinical workflow.
Park: An Open Platform for Learning-Augmented Computer Systems
We present Park, a platform for researchers to experiment with Reinforcement Learning (RL) for computer systems.
Human-centric Metric for Accelerating Pathology Reports Annotation
However, manual classification for a huge number of reports on multiple tasks is labor-intensive.
Machine Learning for Clinical Predictive Analytics
In this chapter, we provide a brief overview of applying machine learning techniques for clinical prediction tasks.
Representation Learning for Electronic Health Records
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.
Multimodal Multitask Representation Learning for Pathology Biobank Metadata Prediction
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.
Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery
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
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
The automatic generation of radiology reports given medical radiographs has significant potential to operationally and improve clinical patient care.
Unsupervised Clinical Language Translation
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.
Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks for Clinical Interpretability
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.
Unsupervised Multimodal Representation Learning across Medical Images and Reports
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.
Towards Unsupervised Speech-to-Text Translation
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.
Mapping Unparalleled Clinical Professional and Consumer Languages with Embedding Alignment
In this work, we utilized the embeddings alignment method for the word mapping between unparalleled clinical professional and consumer language embeddings.
Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces
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)
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Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients
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.
Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval
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.
