1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Weakly-Supervised Domain Adaptation with Proportion-Constrained Pseudo-Labeling
Takumi Okuo, Shinnosuke Matsuo, Shota Harada +2
Domain shift is a significant challenge in machine learning, particularly in medical applications where data distributions differ across institutions due to variations in data coll…
cs.CV2023★ 1 cited
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…
cs.CV2023
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…