7 citations · 8 across the 4 of their papers we have counts for
4 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…
Domain Adaptive Multiple Instance Learning for Instance-level Prediction of Pathological Images
Shusuke Takahama, Yusuke Kurose, Yusuke Mukuta +7
Pathological image analysis is an important process for detecting abnormalities such as cancer from cell images. However, since the image size is generally very large, the cost of…
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…
Multi-Stage Pathological Image Classification using Semantic Segmentation
Shusuke Takahama, Yusuke Kurose, Yusuke Mukuta +5
Histopathological image analysis is an essential process for the discovery of diseases such as cancer. However, it is challenging to train CNN on whole slide images (WSIs) of gigap…