Search Results for author:
Found 24 papers, 7 papers with code
Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learning
Electrocardiogram (ECG) interpretation requires specialized expertise, often involving synthesizing insights from ECG signals with complex clinical queries posed in natural language.
RespLLM: Unifying Audio and Text with Multimodal LLMs for Generalized Respiratory Health Prediction
The high incidence and mortality rates associated with respiratory diseases underscores the importance of early screening.
Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling
Interpreting electrocardiograms (ECGs) and generating comprehensive reports remain challenging tasks in cardiology, often requiring specialized expertise and significant time investment.
Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking
Our pretrained models demonstrate superior performance (against existing acoustic models pretrained with general audio on 16 out of 19 tasks) and generalizability (to unseen datasets and new respiratory audio modalities).
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation
Federated Learning (FL) enables model development by leveraging data distributed across numerous edge devices without transferring local data to a central server.
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation
Federated Learning (FL) enables model development by leveraging data distributed across numerous edge devices without transferring local data to a central server.
Devil in the Landscapes: Inferring Epidemic Exposure Risks from Street View Imagery
Built environment supports all the daily activities and shapes our health.
FairComp: Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing
From a social perspective, we will examine the relationship between fairness and UbiComp research and identify pathways to ensure that ubiquitous technologies do not cause harm or infringe on individual rights.
Cross-device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detection
In this paper, we propose FedLoss, a novel cross-device FL framework for health diagnostics.
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
A Summary of the ComParE COVID-19 Challenges
The COVID-19 pandemic has caused massive humanitarian and economic damage.
Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation
Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19.
Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift
A biosignal is a signal that can be continuously measured from human bodies, such as respiratory sounds, heart activity (ECG), brain waves (EEG), etc, based on which, machine learning models have been developed with very promising performance for automatic disease detection and health status monitoring.
Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark
PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems.
Ranked #2 on
Surgical phase recognition
on HeiChole Benchmark
One-shot Transfer Learning for Population Mapping
Thus, obtaining fine-grained population distribution from coarse-grained distribution becomes an important problem.
Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey
For each problem, we comprehensively introduce the existing DRL solutions.
Sounds of COVID-19: exploring realistic performance of audio-based digital testing
In this paper, we explore the realistic performance of audio-based digital testing of COVID-19.
Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data
To handle these issues, we propose an ensemble framework where multiple deep learning models for sound-based COVID-19 detection are developed from different but balanced subsets from original data.
The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates
The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified.
Reinforced Contact Tracing and Epidemic Intervention
In this paper, we develop an Individual-based Reinforcement Learning Epidemic Control Agent (IDRLECA) to search for smart epidemic control strategies that can simultaneously minimize infections and the cost of mobility intervention.
AttnMove: History Enhanced Trajectory Recovery via Attentional Network
A considerable amount of mobility data has been accumulated due to the proliferation of location-based service.
Rewriting by Generating: Learn Heuristics for Large-scale Vehicle Routing Problems
The large-scale vehicle routing problem is defined based on the classical VRP with usually more than one thousand customers.
Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
This work opens the door to further investigation of how automatically analysed respiratory patterns could be used as pre-screening signals to aid COVID-19 diagnosis.
UrbanRhythm: Revealing Urban Dynamics Hidden in Mobility Data
In this paper, to reveal urban dynamics, we propose a novel system UrbanRhythm to reveal the urban dynamics hidden in human mobility data.
