TP2.L302.5
LEARNABLE SNAKE R-CNN FOR INSTANCE-LEVEL BIOMEDICAL IMAGE SEGMENTATION
Jie Song, Ziyun Cai, Yurong Song, Guoping Jiang, Nanjing University of Posts and Telecommunications, China; Zhichao Lian, Liang Xiao, Nanjing University of Science and Technology, China
Session:
TP2.L302: Biomedical Image Segmentation Lecture
Track:
Applications of Machine Learning
Location:
Room 302
Presentation Time:
Tue, 10 Oct, 17:42 - 18:00 Malaysia Time (UTC +8)
Session Chair:
Benoit Macq, UCLouvain
Session TP2.L302
TP2.L302.1: SEMI-SUPERVISED CONTRASTIVE LEARNING OF GLOBAL AND LOCAL REPRESENTATION FOR 3D MEDICAL IMAGE SEGMENTATION
Chuang Jia, Jian Xue, Ke Lu, Zhongqi Wu, University of Chinese Academy of Sciences, China
TP2.L302.2: SELF-REINFORCING FOR FEW-SHOT MEDICAL IMAGE SEGMENTATION
Yao Huang, Jianming Liu, Hua Chen, Jiangxi Normal University, China
TP2.L302.3: Densely Connected Swin-UNet for Multiscale Information Aggregation in Medical Image Segmentation
Ziyang Wang, Oxford University, United Kingdom of Great Britain and Northern Ireland; Meiwen Su, University of Hong Kong, China; Jian-Qing Zheng, Oxford University, United Kingdom of Great Britain and Northern Ireland; Yang Liu, University of Plymouth, United Kingdom of Great Britain and Northern Ireland
TP2.L302.4: SEGMENTATION AND CLASSIFICATION-BASED DIAGNOSIS OF TUMORS FROM BREAST ULTRASOUND IMAGES USING MULTIBRANCH UNET
Laksath Adityan M K, Himanchal Sharma, Angshuman Paul, Indian Institute of Technology Jodhpur, India
TP2.L302.5: LEARNABLE SNAKE R-CNN FOR INSTANCE-LEVEL BIOMEDICAL IMAGE SEGMENTATION
Jie Song, Ziyun Cai, Yurong Song, Guoping Jiang, Nanjing University of Posts and Telecommunications, China; Zhichao Lian, Liang Xiao, Nanjing University of Science and Technology, China
Contacts