1 code implementation • 7 Jun 2022 • Yusuke Takagi, Noriaki Hashimoto, Hiroki Masuda, Hiroaki Miyoshi, Koichi Ohshima, Hidekata Hontani, Ichiro Takeuchi
In medical image diagnosis, identifying the attention region, i. e., the region of interest for which the diagnosis is made, is an important task.
no code implementations • 8 Jul 2021 • Noriaki Hashimoto, Yusuke Takagi, Hiroki Masuda, Hiroaki Miyoshi, Kei Kohno, Miharu Nagaishi, Kensaku Sato, Mai Takeuchi, Takuya Furuta, Keisuke Kawamoto, Kyohei Yamada, Mayuko Moritsubo, Kanako Inoue, Yasumasa Shimasaki, Yusuke Ogura, Teppei Imamoto, Tatsuzo Mishina, Ken Tanaka, Yoshino Kawaguchi, Shigeo Nakamura, Koichi Ohshima, Hidekata Hontani, Ichiro Takeuchi
To address this problem, we employ attention-based multiple instance learning, which enables us to focus on tumor-specific regions when the similarity between cases is computed.
1 code implementation • CVPR 2020 • Noriaki Hashimoto, Daisuke Fukushima, Ryoichi Koga, Yusuke Takagi, Kaho Ko, Kei Kohno, Masato Nakaguro, Shigeo Nakamura, Hidekata Hontani, Ichiro Takeuchi
We propose a new method for cancer subtype classification from histopathological images, which can automatically detect tumor-specific features in a given whole slide image (WSI).