1 citations · 1 across the 3 of their papers we have counts for
3 papers
Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation
Xiaoqing Liu, Kengo Araki, Shota Harada +7
The domain shift in pathological segmentation is an important problem, where a network trained by a source domain (collected at a specific hospital) does not work well in the targe…
Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification
Shota Harada, Ryoma Bise, Kengo Araki +7
Semi-supervised domain adaptation is a technique to build a classifier for a target domain by modifying a classifier in another (source) domain using many unlabeled samples and a s…
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…