Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation

no code implementations • 26 Apr 2023 • Xiaoqing Liu, Kengo Araki, Shota Harada, Akihiko Yoshizawa, Kazuhiro Terada, Mariyo Kurata, Naoki Nakajima, Hiroyuki Abe, Tetsuo Ushiku, Ryoma Bise

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 target domain (from different hospitals) due to the different image features.

Image Segmentation Semantic Segmentation +2

Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification

no code implementations • 2 Mar 2023 • Shota Harada, Ryoma Bise, Kengo Araki, Akihiko Yoshizawa, Kazuhiro Terada, Mariyo Kurata, Naoki Nakajima, Hiroyuki Abe, Tetsuo Ushiku, Seiichi Uchida

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 small number of labeled samples from the target domain.

Clustering Domain Adaptation +4

Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue

no code implementations • 19 Aug 2021 • Kengo Araki, Mariyo Rokutan-Kurata, Kazuhiro Terada, Akihiko Yoshizawa, Ryoma Bise

Pathological diagnosis is used for examining cancer in detail, and its automation is in demand.