no code implementations • 8 Dec 2023 • Taro Hatsutani, Akimichi Ichinose, Keigo Nakamura, Yoshiro Kitamura
In this paper, we present a novel framework, which can explicitly capture protruded regions in kidneys to enable a better segmentation of kidney tumors.
no code implementations • 8 Dec 2023 • Akimichi Ichinose, Taro Hatsutani, Keigo Nakamura, Yoshiro Kitamura, Satoshi Iizuka, Edgar Simo-Serra, Shoji Kido, Noriyuki Tomiyama
Our framework combines two components of 1) anatomical segmentation of images, and 2) report structuring.
no code implementations • 20 Jun 2022 • Yoshihide Sawada, Keigo Nakamura
In this study, we use a self-explaining neural network (SENN), which learns unsupervised concepts, to acquire concepts that are easy for people to understand automatically.
1 code implementation • 3 Feb 2022 • Yoshihide Sawada, Keigo Nakamura
We refer to the proposed model as the concept bottleneck model with additional unsupervised concepts (CBM-AUC).
no code implementations • Findings of the Association for Computational Linguistics 2020 • Toru Nishino, Ryota Ozaki, Yohei Momoki, Tomoki Taniguchi, Ryuji Kano, Norihisa Nakano, Yuki Tagawa, Motoki Taniguchi, Tomoko Ohkuma, Keigo Nakamura
We propose a novel reinforcement learning method with a reconstructor to improve the clinical correctness of generated reports to train the data-to-text module with a highly imbalanced dataset.
no code implementations • 29 Sep 2020 • Naoto Masuzawa, Yoshiro Kitamura, Keigo Nakamura, Satoshi Iizuka, Edgar Simo-Serra
The input to the second networks have an auxiliary channel in addition to the 3D CT images.
no code implementations • 11 Apr 2020 • Keigo Nakamura, Yoshiro Suzuki
In this study, we propose a new deep learning model to generate an optimized structure for a given design domain and other boundary conditions without iteration.