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This work investigates Artificial Intelligence (AI) systems that detect respiratory insufficiency (RI) by analyzing speech audios, thus treating speech as a RI biomarker. Previous works collected RI data (P1) from COVID-19 patients during…

The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries. With the continuous growth of new infections, developing automated tools for COVID-19 identification with CT…

Image and Video Processing · Electrical Eng. & Systems 2020-09-17 Zhao Wang , Quande Liu , Qi Dou

Just like your phone can detect what song is playing in crowded spaces, we show that Artificial Intelligence transfer learning algorithms trained on cough phone recordings results in diagnostic tests for COVID-19. To gain adoption by the…

This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopt advanced deep network architectures and propose a transfer learning…

Image and Video Processing · Electrical Eng. & Systems 2021-01-19 Hammam Alshazly , Christoph Linse , Erhardt Barth , Thomas Martinetz

We introduce a comprehensive screening platform for the COVID-19 (a.k.a., SARS-CoV-2) pneumonia. The proposed AI-based system works on chest x-ray (CXR) images to predict whether a patient is infected with the COVID-19 disease. Although the…

With the rapid development of COVID-19 into a global pandemic, there is an ever more urgent need for cheap, fast and reliable tools that can assist physicians in diagnosing COVID-19. Medical imaging such as CT can take a key role in…

Image and Video Processing · Electrical Eng. & Systems 2021-01-26 Jannis Born , Gabriel Brändle , Manuel Cossio , Marion Disdier , Julie Goulet , Jérémie Roulin , Nina Wiedemann

COVID-19 continues to cause a significant impact on public health. To minimize this impact, policy makers undertake containment measures that however, when carried out disproportionately to the actual threat, as a result if errorneous…

Purpose: The need to streamline patient management for COVID-19 has become more pressing than ever. Chest X-rays provide a non-invasive (potentially bedside) tool to monitor the progression of the disease. In this study, we present a…

Image and Video Processing · Electrical Eng. & Systems 2020-07-01 Joseph Paul Cohen , Lan Dao , Paul Morrison , Karsten Roth , Yoshua Bengio , Beiyi Shen , Almas Abbasi , Mahsa Hoshmand-Kochi , Marzyeh Ghassemi , Haifang Li , Tim Q Duong

This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes the different GANs methods and the lungs images datasets for…

Image and Video Processing · Electrical Eng. & Systems 2022-05-17 Hazrat Ali , Zubair Shah

In this paper, we present our comprehensive study aimed at enhancing the generalization capabilities of audio deepfake detection models. We investigate the performance of various pre-trained backbones, including Wav2Vec2, WavLM, and…

Audio and Speech Processing · Electrical Eng. & Systems 2025-07-03 Jose A. Lopez , Georg Stemmer , Héctor Cordourier Maruri

Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, sharing diagnostic images across medical institutions is usually not allowed due to…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-10-27 Weishan Zhang , Tao Zhou , Qinghua Lu , Xiao Wang , Chunsheng Zhu , Haoyun Sun , Zhipeng Wang , Sin Kit Lo , Fei-Yue Wang

Depression is a common mental disorder which has been affecting millions of people around the world and becoming more severe with the arrival of COVID-19. Nevertheless proper diagnosis is not accessible in many regions due to a severe…

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a…

Audio-language pretraining holds promise for general-purpose audio understanding, yet remains underexplored compared to its vision counterpart. While vision-language models like CLIP serve as widely adopted foundations, existing…

Audio and Speech Processing · Electrical Eng. & Systems 2025-11-24 Wei-Cheng Tseng , Xuanru Zhou , Mingyue Huo , Yiwen Shao , Hao Zhang , Dong Yu

The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of people and is still rapidly spreading all over the globe. Most COVID-19 patients suffer from lung infection, so one important diagnostic method is to…

Image and Video Processing · Electrical Eng. & Systems 2020-05-05 Yifan Zhang , Shuaicheng Niu , Zhen Qiu , Ying Wei , Peilin Zhao , Jianhua Yao , Junzhou Huang , Qingyao Wu , Mingkui Tan

As a lightweight and non-invasive imaging technique, lung ultrasound (LUS) has gained importance for assessing lung pathologies. The use of Artificial intelligence (AI) in medical decision support systems is promising due to the time- and…

Due to the large accumulation of patients requiring hospitalization, the COVID-19 pandemic disease caused a high overload of health systems, even in developed countries. Deep learning techniques based on medical imaging data can help in the…

Because of the rapid spread of COVID-19 to almost every part of the globe, huge volumes of data and case studies have been made available, providing researchers with a unique opportunity to find trends and make discoveries like never…

Machine Learning · Computer Science 2021-10-20 Sarwan Ali , Yijing Zhou , Murray Patterson

Artificial Intelligence (AI)-based models can help in diagnosing COVID-19 from lung CT scans and X-ray images; however, these models require large amounts of data for training and validation. Many researchers studied Generative Adversarial…

Image and Video Processing · Electrical Eng. & Systems 2023-04-10 Hazrat Ali , Christer Gronlund , Zubair Shah

Masked Autoencoders (MAEs) trained on audio spectrogram patches have emerged as a prominent approach for learning self-supervised audio representations. While several recent papers have evaluated key aspects of training MAEs on audio data,…

Sound · Computer Science 2025-07-15 Sarthak Yadav , Sergios Theodoridis , Zheng-Hua Tan