455 citations · 685 across the 20 of their papers we have counts for
21 papers
WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning
Yuwei Zhang, Tong Xia, Bianca Emmerich +5
Language models are remarkably capable at medical question answering, in some cases surpassing the accuracy of general physicians. However, answering questions about wearable healt…
Wearable Foundation Models Should Go Beyond Static Encoders
Yu Yvonne Wu, Yuwei Zhang, Hyungjun Yoon +8
Wearable foundation models (WFMs), trained on large volumes of data collected by affordable, always-on devices, have demonstrated strong performance on short-term, well-defined hea…
RAMoEA-QA: Hierarchical Specialization for Robust Respiratory Audio Question Answering
Gaia A. Bertolino, Yuwei Zhang, Tong Xia +2
Conversational generative AI is increasingly explored in healthcare, where models must integrate heterogeneous patient signals and support diverse interaction styles while producin…
RA-QA: A Benchmarking System for Respiratory Audio Question Answering Under Real-World Heterogeneity
Gaia A. Bertolino, Yuwei Zhang, Tong Xia +2
As conversational multimodal AI tools are increasingly adopted to process patient data for health assessment, robust benchmarks are needed to measure progress and expose failure mo…
RespLLM: Unifying Audio and Text with Multimodal LLMs for Generalized Respiratory Health Prediction
Yuwei Zhang, Tong Xia, Aaqib Saeed +1
The high incidence and mortality rates associated with respiratory diseases underscores the importance of early screening. Machine learning models can automate clinical consultatio…
Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learning
Jialu Tang, Tong Xia, Yuan Lu +2
Electrocardiogram (ECG) interpretation requires specialized expertise, often involving synthesizing insights from ECG signals with complex clinical queries posed in natural languag…
Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling
Jialu Tang, Tong Xia, Yuan Lu +2
Interpreting electrocardiograms (ECGs) and generating comprehensive reports remain challenging tasks in cardiology, often requiring specialized expertise and significant time inves…
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation
Tong Xia, Abhirup Ghosh, Xinchi Qiu +1
Federated Learning (FL) enables model development by leveraging data distributed across numerous edge devices without transferring local data to a central server. However, existing…
Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking
Yuwei Zhang, Tong Xia, Jing Han +6
Respiratory audio, such as coughing and breathing sounds, has predictive power for a wide range of healthcare applications, yet is currently under-explored. The main problem for th…
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation
Tong Xia, Abhirup Ghosh, Xinchi Qiu +1
Federated Learning (FL) enables model development by leveraging data distributed across numerous edge devices without transferring local data to a central server. However, existing…
Cross-device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detection
Tong Xia, Jing Han, Abhirup Ghosh +1
Federated learning (FL) aided health diagnostic models can incorporate data from a large number of personal edge devices (e.g., mobile phones) while keeping the data local to the o…
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
Chinedu Innocent Nwoye, Deepak Alapatt, Tong Yu +59
Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works…
A Summary of the ComParE COVID-19 Challenges
Harry Coppock, Alican Akman, Christian Bergler +17
The COVID-19 pandemic has caused massive humanitarian and economic damage. Teams of scientists from a broad range of disciplines have searched for methods to help governments and c…
Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift
Tong Xia, Jing Han, Cecilia Mascolo
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 learn…
Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation
Ting Dang, Jing Han, Tong Xia +9
Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection a…
Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark
Martin Wagner, Beat-Peter Müller-Stich, Anna Kisilenko +38
PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the…
Sounds of COVID-19: exploring realistic performance of audio-based digital testing
Jing Han, Tong Xia, Dimitris Spathis +9
Researchers have been battling with the question of how we can identify Coronavirus disease (COVID-19) cases efficiently, affordably and at scale. Recent work has shown how audio b…
Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data
Tong Xia, Jing Han, Lorena Qendro +2
Recently, sound-based COVID-19 detection studies have shown great promise to achieve scalable and prompt digital pre-screening. However, there are still two unsolved issues hinderi…
The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates
Björn W. Schuller, Anton Batliner, Christian Bergler +21
The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the CO…
Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data
Jing Han, Chloë Brown, Jagmohan Chauhan +6
The development of fast and accurate screening tools, which could facilitate testing and prevent more costly clinical tests, is key to the current pandemic of COVID-19. In this con…
Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
Chloë Brown, Jagmohan Chauhan, Andreas Grammenos +6
Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or a…