Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data

• URL: http://arxiv.org/abs/2104.02005v1
• Date: Mon, 5 Apr 2021 16:54:03 GMT
• Title: Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data
• Authors: Tong Xia, Jing Han, Lorena Qendro, Ting Dang, Cecilia Mascolo
• Abstract summary: We propose an ensemble framework where multiple deep learning models for sound-based COVID-19 detection are developed. It is shown that false predictions often yield higher uncertainty. This study paves the way for a more robust sound-based COVID-19 automated screening system.
• Score: 15.833328435820622
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 hindering the practice. First, collected datasets for model training are often imbalanced, with a considerably smaller proportion of users tested positive, making it harder to learn representative and robust features. Second, deep learning models are generally overconfident in their predictions. Clinically, false predictions aggravate healthcare costs. Estimation of the uncertainty of screening would aid this. 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. As such, data are utilized more effectively compared to traditional up-sampling and down-sampling approaches: an AUC of 0.74 with a sensitivity of 0.68 and a specificity of 0.69 is achieved. Simultaneously, we estimate uncertainty from the disagreement across multiple models. It is shown that false predictions often yield higher uncertainty, enabling us to suggest the users with certainty higher than a threshold to repeat the audio test on their phones or to take clinical tests if digital diagnosis still fails. This study paves the way for a more robust sound-based COVID-19 automated screening system.

Related papers

• Conservative Prediction via Data-Driven Confidence Minimization [70.93946578046003]
  In safety-critical applications of machine learning, it is often desirable for a model to be conservative. We propose the Data-Driven Confidence Minimization framework, which minimizes confidence on an uncertainty dataset.
  arXiv  Detail & Related papers  (2023-06-08T07:05:36Z)
• Developing a multi-variate prediction model for the detection of COVID-19 from Crowd-sourced Respiratory Voice Data [0.0]
  The novelty of this work is in the development of a deep learning model for the identification of COVID-19 patients from voice recordings. We used the Cambridge University dataset consisting of 893 audio samples, crowd-sourced from 4352 participants that used a COVID-19 Sounds app. Based on the voice data, we developed deep learning classification models to detect positive COVID-19 cases.
  arXiv  Detail & Related papers  (2022-09-08T11:46:37Z)
• Learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19 [17.781861866125023]
  Using data from a major healthcare provider, we develop survival models predicting severe COVID-19 progression. We develop two sets of high-performance risk scores: (i) an unconstrained model built from all available features; and (ii) a pipeline that learns a small set of clinical concepts before training a risk predictor.
  arXiv  Detail & Related papers  (2022-08-28T02:59:35Z)
• Sounds of COVID-19: exploring realistic performance of audio-based digital testing [17.59710651224251]
  In this paper, we explore the realistic performance of audio-based digital testing of COVID-19. We collected a large crowdsourced respiratory audio dataset through a mobile app, alongside recent COVID-19 test result and symptoms intended as a ground truth. The unbiased model takes features extracted from breathing, coughs, and voice signals as predictors and yields an AUC-ROC of 0.71 (95% CI: 0.65$-$0.77)
  arXiv  Detail & Related papers  (2021-06-29T15:50:36Z)
• Continual Learning for Fake Audio Detection [62.54860236190694]
  This paper proposes detecting fake without forgetting, a continual-learning-based method, to make the model learn new spoofing attacks incrementally. Experiments are conducted on the ASVspoof 2019 dataset.
  arXiv  Detail & Related papers  (2021-04-15T07:57:05Z)
• Audio feature ranking for sound-based COVID-19 patient detection [1.7188280334580195]
  COVID-19 has emerged as a low-cost, non-invasive, and accessible audio classification method. No application has been approved for official use due to the stringent reliability and accuracy requirements of the critical healthcare setting. We performed an investigation and ranking of 15 audio features, including less well-known ones. The results were verified on two independent COVID-19 sound datasets.
  arXiv  Detail & Related papers  (2021-04-14T21:06:20Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• Virufy: A Multi-Branch Deep Learning Network for Automated Detection of COVID-19 [1.9899603776429056]
  Researchers have successfully presented models for detecting COVID-19 infection status using audio samples recorded in clinical settings. We propose a multi-branch deep learning network that is trained and tested on crowdsourced data where most of the data has not been manually processed and cleaned.
  arXiv  Detail & Related papers  (2021-03-02T15:31:09Z)
• End-2-End COVID-19 Detection from Breath & Cough Audio [68.41471917650571]
  We demonstrate the first attempt to diagnose COVID-19 using end-to-end deep learning from a crowd-sourced dataset of audio samples. We introduce a novel modelling strategy using a custom deep neural network to diagnose COVID-19 from a joint breath and cough representation.
  arXiv  Detail & Related papers  (2021-01-07T01:13:00Z)
• Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks [68.8204255655161]
  We adapt an ensemble of Convolutional Neural Networks to classify if a speaker is infected with COVID-19 or not. Ultimately, it achieves an Unweighted Average Recall (UAR) of 74.9%, or an Area Under ROC Curve (AUC) of 80.7% by ensembling neural networks.
  arXiv  Detail & Related papers  (2020-12-29T01:14:17Z)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.