no code implementations • 25 Apr 2024 • Hiroyuki Hanada, Satoshi Akahane, Tatsuya Aoyama, Tomonari Tanaka, Yoshito Okura, Yu Inatsu, Noriaki Hashimoto, Taro Murayama, Lee Hanju, Shinya Kojima, Ichiro Takeuchi
In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting.
1 code implementation • 19 Oct 2023 • Joshua Butke, Noriaki Hashimoto, Ichiro Takeuchi, Hiroaki Miyoshi, Koichi Ohshima, Jun Sakuma
Whole-slide image analysis via the means of computational pathology often relies on processing tessellated gigapixel images with only slide-level labels available.
no code implementations • 22 Jun 2023 • Hiroyuki Hanada, Noriaki Hashimoto, Kouichi Taji, Ichiro Takeuchi
Among the class of ML methods known as linear estimators, there exists an efficient model update framework called the low-rank update that can effectively handle changes in a small number of rows and columns within the data matrix.
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).
no code implementations • CVPR 2020 • Kosuke Tanizaki, Noriaki Hashimoto, Yu Inatsu, Hidekata Hontani, Ichiro Takeuchi
To overcome this difficulty, we introduce a statistical approach called selective inference, and develop a framework to compute valid p-values in which the segmentation bias is properly accounted for.