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.
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.
no code implementations • 6 Nov 2021 • Shota Harada, Ryoma Bise, Hideaki Hayashi, Kiyohito Tanaka, Seiichi Uchida
Ulcerative colitis (UC) classification, which is an important task for endoscopic diagnosis, involves two main difficulties.
1 code implementation • 24 Jan 2020 • Daichi Haraguchi, Shota Harada, Brian Kenji Iwana, Yuto Shinahara, Seiichi Uchida
Moreover, we analyzed the relationship between character classes and font identification accuracy.
no code implementations • 17 May 2019 • Shota Harada, Hideaki Hayashi, Seiichi Uchida
GAN-based generative models only learn the projection between a random distribution as input data and the distribution of training data. Therefore, the relationship between input and generated data is unclear, and the characteristics of the data generated from this model cannot be controlled.