5 code implementations • 10 Apr 2022 • Chinedu Innocent Nwoye, Deepak Alapatt, Tong Yu, Armine Vardazaryan, Fangfang Xia, Zixuan Zhao, Tong Xia, Fucang Jia, Yuxuan Yang, Hao Wang, Derong Yu, Guoyan Zheng, Xiaotian Duan, Neil Getty, Ricardo Sanchez-Matilla, Maria Robu, Li Zhang, Huabin Chen, Jiacheng Wang, Liansheng Wang, Bokai Zhang, Beerend Gerats, Sista Raviteja, Rachana Sathish, Rong Tao, Satoshi Kondo, Winnie Pang, Hongliang Ren, Julian Ronald Abbing, Mohammad Hasan Sarhan, Sebastian Bodenstedt, Nithya Bhasker, Bruno Oliveira, Helena R. Torres, Li Ling, Finn Gaida, Tobias Czempiel, João L. Vilaça, Pedro Morais, Jaime Fonseca, Ruby Mae Egging, Inge Nicole Wijma, Chen Qian, GuiBin Bian, Zhen Li, Velmurugan Balasubramanian, Debdoot Sheet, Imanol Luengo, Yuanbo Zhu, Shuai Ding, Jakob-Anton Aschenbrenner, Nicolas Elini van der Kar, Mengya Xu, Mobarakol Islam, Lalithkumar Seenivasan, Alexander Jenke, Danail Stoyanov, Didier Mutter, Pietro Mascagni, Barbara Seeliger, Cristians Gonzalez, Nicolas Padoy
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)
no code implementations • 17 Feb 2022 • Harry Coppock, Alican Akman, Christian Bergler, Maurice Gerczuk, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Jing Han, Shahin Amiriparian, Alice Baird, Lukas Stappen, Sandra Ottl, Panagiotis Tzirakis, Anton Batliner, Cecilia Mascolo, Björn W. Schuller
The COVID-19 pandemic has caused massive humanitarian and economic damage.
no code implementations • 4 Jan 2022 • Ting Dang, Jing Han, Tong Xia, Dimitris Spathis, Erika Bondareva, Chloë Siegele-Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Andres Floto, Pietro Cicuta, Cecilia Mascolo
Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19.
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.
no code implementations • 30 Sep 2021 • Martin Wagner, Beat-Peter Müller-Stich, Anna Kisilenko, Duc Tran, Patrick Heger, Lars Mündermann, David M Lubotsky, Benjamin Müller, Tornike Davitashvili, Manuela Capek, Annika Reinke, Tong Yu, Armine Vardazaryan, Chinedu Innocent Nwoye, Nicolas Padoy, Xinyang Liu, Eung-Joo Lee, Constantin Disch, Hans Meine, Tong Xia, Fucang Jia, Satoshi Kondo, Wolfgang Reiter, Yueming Jin, Yonghao Long, Meirui Jiang, Qi Dou, Pheng Ann Heng, Isabell Twick, Kadir Kirtac, Enes Hosgor, Jon Lindström Bolmgren, Michael Stenzel, Björn von Siemens, Hannes G. Kenngott, Felix Nickel, Moritz von Frankenberg, Franziska Mathis-Ullrich, Lena Maier-Hein, Stefanie Speidel, Sebastian Bodenstedt
PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems.
Thus, obtaining fine-grained population distribution from coarse-grained distribution becomes an important problem.
For each problem, we comprehensively introduce the existing DRL solutions.
no code implementations • 29 Jun 2021 • Jing Han, Tong Xia, Dimitris Spathis, Erika Bondareva, Chloë Brown, Jagmohan Chauhan, Ting Dang, Andreas Grammenos, Apinan Hasthanasombat, Andres Floto, Pietro Cicuta, Cecilia Mascolo
In this paper, we explore the realistic performance of audio-based digital testing of COVID-19.
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.
no code implementations • 24 Feb 2021 • Björn W. Schuller, Anton Batliner, Christian Bergler, Cecilia Mascolo, Jing Han, Iulia Lefter, Heysem Kaya, Shahin Amiriparian, Alice Baird, Lukas Stappen, Sandra Ottl, Maurice Gerczuk, Panagiotis Tzirakis, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Leon J. M. Rothkrantz, Joeri Zwerts, Jelle Treep, Casper Kaandorp
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.
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.
A considerable amount of mobility data has been accumulated due to the proliferation of location-based service.
The large-scale vehicle routing problem is defined based on the classical VRP with usually more than one thousand customers.
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.
In this paper, to reveal urban dynamics, we propose a novel system UrbanRhythm to reveal the urban dynamics hidden in human mobility data.