8 citations · 27 across the 22 of their papers we have counts for
3 papers · 1 filter
Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation
Xiaoqing Liu, Kenji Ono, Ryoma Bise
The development of medical image segmentation using deep learning can significantly support doctors' diagnoses. Deep learning needs large amounts of data for training, which also r…
Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue
Kengo Araki, Mariyo Rokutan-Kurata, Kazuhiro Terada +2
Pathological diagnosis is used for examining cancer in detail, and its automation is in demand. To automatically segment each cancer area, a patch-based approach is usually used si…
Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response
Kazuya Nishimura, Dai Fei Elmer Ker, Ryoma Bise
Cell shape analysis is important in biomedical research. Deep learning methods may perform to segment individual cells if they use sufficient training data that the boundary of eac…