2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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COVID-IP-1: COVID Related Image Processing 1

Session Type: Poster
Time: Monday, September 20, 15:30 - 17:00
Location: Area C
Session Chair: Arash Mohammadi, Concordia University
 
   COVID-IP-1.1: EMPLOYING ACOUSTIC FEATURES TO AID NEURAL NETWORKS TOWARDS PLATFORM AGNOSTIC LEARNING IN LUNG ULTRASOUND IMAGING
         Mahesh Raveendranatha Panicker; Indian Institute of Technology Palakkad
         Yale Tung Chen; Hospital Universitario La Paz, Madrid
         Gayathri M; Indian Institute of Technology Palakkad
         Madhavanunni A N; Indian Institute of Technology Palakkad
         Kiran Vishnu Narayan; Government Medical College, Kottayam
         Kesavadas C; Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum
         Vinod A P; Indian Institute of Technology Palakkad
 
   COVID-IP-1.2: DEEP ACTIVE LEARNING FOR FIBROSIS SEGMENTATION OF CHEST CT SCANS FROM COVID-19 PATIENTS
         Xiaohong Liu; Tsinghua University
         Kai Wang; University of California, San Diego
         Ting Chen; Tsinghua University
 
   COVID-IP-1.3: HYBRID DEEP LEARNING MODEL FOR DIAGNOSIS OF COVID-19 USING CT SCANS AND CLINICAL/DEMOGRAPHIC DATA
         Parnian Afshar; Concordia University
         Shahin Heidarian; Concordia University
         Farnoosh Naderkhani; Concordia University
         Moezedin Javad Rafiee; McGill University
         Anastasia Oikonomou; University of Toronto
         Konstantinos N. Plataniotis; University of Toronto
         Arash Mohammadi; Concordia University
 
   COVID-IP-1.4: RELIABLE COVID-19 DETECTION USING CHEST X-RAY IMAGES
         Aysen Degerli; Tampere University
         Mete Ahishali; Tampere University
         Serkan Kiranyaz; Qatar University
         Muhammad E. H. Chowdhury; Qatar University
         Moncef Gabbouj; Tampere University
 
   COVID-IP-1.5: MMFC: MULTI-MODAL FUSION CASCADE FRAMEWORK FOR COVID-19 DISEASE COURSE CLASSIFICATION
         Han Yang; Chongqing University
         Mengke Zhang; Chongqing University
         Lu Shen; Chongqing University
         Qiuli Wang; Chongqing University
         Wanqiu Cheng; Chongqing University
         Chen Liu; The First Affiliated Hospital of Army Medical University
         Minjian Hong; Chongqing University
 
   COVID-IP-1.6: POCFORMER: A LIGHTWEIGHT TRANSFORMER ARCHITECTURE FOR DETECTION OF COVID-19 USING POINT OF CARE ULTRASOUND
         Shehan Perera; The Ohio State University
         Srikar Adhikari; University of Arizona
         Alper Yilmaz; The Ohio State University
 
   COVID-IP-1.7: FEATURES OF ICU ADMISSION IN X-RAY IMAGES OF COVID-19 PATIENTS
         Douglas Pinto Sampaio Gomes; Machine Vision and Digital Health (MAVIDH) Research group, Charles Sturt University
         Anwaar Ulhaq; Machine Vision and Digital Health (MAVIDH) Research group, Charles Sturt University
         Manoranjan Paul; Machine Vision and Digital Health (MAVIDH) Research group, Charles Sturt University
         Michael Horry; Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney
         Subrata Chakraborty; Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney
         Manash Saha; Manning Rural Referral Hospital
         Tanmoy Debnath; Machine Vision and Digital Health (MAVIDH) Research group, Charles Sturt University
         D.M. Motiur Rahaman; Charles Sturt University
 
   COVID-IP-1.8: EXPLOITING DEEP CROSS-SLICE FEATURES FROM CT IMAGES FOR MULTI-CLASS PNEUMONIA CLASSIFICATION
         Jiawang Cao; Fudan University
         Lulu Jiang; Fudan University
         Junlin Hou; Fudan University
         Longquan Jiang; Fudan University
         Ruiwei Zhao; Fudan University
         Weiya Shi; Fudan University
         Fei Shan; Fudan University
         Rui Feng; Fudan University
 
   COVID-IP-1.9: BOOSTING DEEP TRANSFER LEARNING FOR COVID-19 CLASSIFICATION
         Fouzia Altaf; Edith Cowan University
         Syed M.S. Islam; Edith Cowan University
         Naeem K. Janjua; Edith Cowan University
         Naveed Akhtar; University of Western Australia
 
   COVID-IP-1.10: PAIRFLOW: ENHANCING PORTABLE CHEST X-RAY BY FLOW-BASED DEFORMATION FOR COVID-19 DIAGNOSING
         Ngan Le; University of Arkansas
         James Sorensen; UAMS Medical College
         Toan Duc Bui; VinAI Research
         Arabinda Choudhary; UAMS Medical College
         Khoa Luu; University of Arkansas
         Hien Nguyen; University of Houston