Technical Program

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BIO-P6: Biomedical Image Segmentation

Session Type: Poster
Time: Friday, 8 May, 15:15 - 17:15
Location: On-Demand
Virtual Session: View on Virtual Platform
Session Chair: Martin Maška, Masaryk University
 
 BIO-P6.1: A SEGMENTATION BASED ROBUST DEEP LEARNING FRAMEWORK FOR MULTIMODAL RETINAL IMAGE REGISTRATION
         Yiqian Wang; University of California, San Diego
         Junkang Zhang; University of California, San Diego
         Cheolhong An; University of California, San Diego
         Melina Cavichini; University of California, San Diego
         Mahima Jhingan; University of California, San Diego
         Manuel J. Amador-Patarroyo; University of California, San Diego
         Christopher P. Long; University of California, San Diego
         Dirk-Uwe G. Bartsch; University of California, San Diego
         William R. Freeman; University of California, San Diego
         Truong Q. Nguyen; University of California, San Diego
 
 BIO-P6.2: DENSE RESIDUAL NETWORK FOR RETINAL VESSEL SEGMENTATION
         Changlu Guo; Budapest University of Technology and Economics
         Márton Szemenyei; Budapest University of Technology and Economics
         Yugen Yi; Jiangxi Normal University
         Ying Xue; Eötvös Loránd University
         Wei Zhou; Shenyang Aerospace University
         Yangyuan Li; Budapest University of Technology and Economics
 
 BIO-P6.3: LIGHTWEIGHT V-NET FOR LIVER SEGMENTATION
         Tao Lei; Shaanxi University of Science and Technology
         Wenzheng Zhou; Shaanxi University of Science and Technology
         Yuxiao Zhang; Shaanxi University of Science and Technology
         Risheng Wang; Shaanxi University of Science and Technology
         Hongying Meng; Brunel University London
         Asoke K. Nandi; Brunel University London
 
 BIO-P6.4: ACU-NET: A 3D ATTENTION CONTEXT U-NET FOR MULTIPLE SCLEROSIS LESION SEGMENTATION
         Chuan Hu; Ministry of Education Beijing University of Posts and Telecommunications
         Guixia Kang; Ministry of Education Beijing University of Posts and Telecommunications
         Beibei Hou; Ministry of Education Beijing University of Posts and Telecommunications
         Yiyuan Ma; Ministry of Education Beijing University of Posts and Telecommunications
         Fabrice Labeau; McGill University
         Zichen Su; Ministry of Education Beijing University of Posts and Telecommunications
 
 BIO-P6.5: EDNFC-NET: CONVOLUTIONAL NEURAL NETWORK WITH NESTED FEATURE CONCATENATION FOR NUCLEI-INSTANCE SEGMENTATION
         Shiv Gehlot; Indraprastha Institute of Information Technology Delhi
         Anubha Gupta; Indraprastha Institute of Information Technology Delhi
         Ritu Gupta; All India Institute of Medical Sciences, New Delhi
 
 BIO-P6.6: AN UNSUPERVISED RETINAL VESSEL EXTRACTION AND SEGMENTATION METHOD BASED ON A TUBE MARKED POINT PROCESS MODEL
         Tianyu Li; Purdue University
         Mary Comer; Purdue University
         Josiane Zerubia; Inria and Université Côte d'Azur
 
 BIO-P6.7: KALM: KEY AREA LOCALIZATION MECHANISM FOR ABNORMALITY DETECTION IN MUSCULOSKELETAL RADIOGRAPHS
         Wei Huang; Northwestern Polytechnical University
         Zhitong Xiong; Northwestern Polytechnical University
         Qi Wang; Northwestern Polytechnical University
         Xuelong Li; Northwestern Polytechnical University
 
 BIO-P6.8: COMBINING CGAN AND MIL FOR HOTSPOT SEGMENTATION IN BONE SCINTIGRAPHY
         Hang Xu; Shanghai Jiao Tong University
         Shijie Geng; Shanghai Jiao Tong University
         Yu Qiao; Shanghai Jiao Tong University
         Kuan Xu; Shanghai Jiao Tong University
         Yueyang Gu; Shanghai Jiao Tong University
 
 BIO-P6.9: A NONINVASIVE METHOD TO DETECT DIABETES MELLITUS AND LUNG CANCER USING THE STACKED SPARSE AUTOENCODER
         Qi Zhang; University of Macau
         Jianhang Zhou; University of Macau
         Bob Zhang; University of Macau
 
 BIO-P6.10: A MULTI-SCALED RECEPTIVE FIELD LEARNING APPROACH FOR MEDICAL IMAGE SEGMENTATION
         Pengcheng Guo; Inner Mongolia University
         Xiangdong Su; Inner Mongolia University
         Haoran Zhang; Inner Mongolia University
         Meng Wang; Inner Mongolia University
         Feilong Bao; Inner Mongolia University
 
 BIO-P6.11: AUTOMATIC DATA AUGMENTATION VIA DEEP REINFORCEMENT LEARNING FOR EFFECTIVE KIDNEY TUMOR SEGMENTATION
         Tiexin Qin; Nanjing University
         Ziyuan Wang; Nanjing University
         Kelei He; Nanjing University
         Yinghuan Shi; Nanjing University
         Yang Gao; Nanjing University
         Dinggang Shen; University of North Carolina - Chapel Hill
 
 BIO-P6.12: CROSS-STAINED SEGMENTATION FROM RENAL BIOPSY IMAGES USING MULTI-LEVEL ADVERSARIAL LEARNING
         Ke Mei; Beijing University of Posts and Telecommunications
         Chuang Zhu; Beijing University of Posts and Telecommunications
         Lei Jiang; Peking University People's Hospital
         Jun Liu; Beijing University of Posts and Telecommunications
         Yuanyuan Qiao; Beijing University of Posts and Telecommunications