no code implementations • 15 Dec 2023 • Kazuma Kobayashi, Yasuyuki Takamizawa, Mototaka Miyake, Sono Ito, Lin Gu, Tatsuya Nakatsuka, Yu Akagi, Tatsuya Harada, Yukihide Kanemitsu, Ryuji Hamamoto
We hypothesize that superior attention maps should align with the information that physicians focus on, potentially reducing prediction uncertainty and increasing model reliability.
no code implementations • 6 Sep 2023 • Mengliang Zhang, Xinyue Hu, Lin Gu, Liangchen Liu, Kazuma Kobayashi, Tatsuya Harada, Ronald M. Summers, Yingying Zhu
In this paper, we re-extract disease labels from CXR reports to make them more realistic by considering disease severity and uncertainty in classification.
no code implementations • 15 Aug 2023 • Kazuma Kobayashi, Syed Bahauddin Alam
This study marks an important step towards harnessing the power of surrogate modeling techniques in critical engineering domains.
1 code implementation • 22 Jul 2023 • Xinyue Hu, Lin Gu, Qiyuan An, Mengliang Zhang, Liangchen Liu, Kazuma Kobayashi, Tatsuya Harada, Ronald M. Summers, Yingying Zhu
Given a pair of main and reference images, this task attempts to answer several questions on both diseases and, more importantly, the differences between them.
1 code implementation • 7 Mar 2023 • Kazuma Kobayashi, Lin Gu, Ryuichiro Hataya, Takaaki Mizuno, Mototaka Miyake, Hirokazu Watanabe, Masamichi Takahashi, Yasuyuki Takamizawa, Yukihiro Yoshida, Satoshi Nakamura, Nobuji Kouno, Amina Bolatkan, Yusuke Kurose, Tatsuya Harada, Ryuji Hamamoto
As a result, our SBMIR system enabled users to overcome previous challenges, including image retrieval based on fine-grained image characteristics, image retrieval without example images, and image retrieval for isolated samples.
no code implementations • 19 Feb 2023 • Xinyue Hu, Lin Gu, Kazuma Kobayashi, Qiyuan An, Qingyu Chen, Zhiyong Lu, Chang Su, Tatsuya Harada, Yingying Zhu
Medical visual question answering (VQA) aims to answer clinically relevant questions regarding input medical images.
no code implementations • 17 Jan 2023 • Kazuma Kobayashi, James Daniell, Syed Bahauddin Alam
Neural Operator Networks (ONets) represent a novel advancement in machine learning algorithms, offering a robust and generalizable alternative for approximating partial differential equations (PDEs) solutions.
no code implementations • 17 Jan 2023 • Kazuma Kobayashi, Syed Bahauddin Alam
Therefore, the use of explainable AI (XAI) and interpretable machine learning (IML) is crucial for the accurate prediction of prognostics, such as remaining useful life (RUL), in a digital twin system, to make it intelligent while ensuring that the AI model is transparent in its decision-making processes and that the predictions it generates can be understood and trusted by users.
Decision Making Explainable Artificial Intelligence (XAI) +1
no code implementations • 23 Nov 2022 • James Daniell, Kazuma Kobayashi, Susmita Naskar, Dinesh Kumar, Souvik Chakraborty, Ayodeji Alajo, Ethan Taber, Joseph Graham, Syed Alam
In order to address this gap, this study specifically focuses on the "ML-driven prediction algorithms" as a viable component for the nuclear reactor operation while assessing the reliability and efficacy of the proposed model.
no code implementations • 14 Oct 2022 • Kazuma Kobayashi, Dinesh Kumar, Matthew Bonney, Souvik Chakraborty, Kyle Paaren, Syed Alam
To understand the potential of intelligent confirmatory tools, the U. S. Nuclear Regulatory Committee (NRC) initiated a future-focused research project to assess the regulatory viability of machine learning (ML) and artificial intelligence (AI)-driven Digital Twins (DTs) for nuclear power applications.
no code implementations • 30 Sep 2022 • M. Rahman, Abid Khan, Sayeed Anowar, Md Al-Imran, Richa Verma, Dinesh Kumar, Kazuma Kobayashi, Syed Alam
After that, a detailed overview of uncertainties, uncertainty quantification frameworks, and specifics of uncertainty quantification methodologies for a surrogate model linked to a digital twin is presented.
no code implementations • 25 Sep 2022 • Md. Shamim Hassan, Abid Hossain Khan, Richa Verma, Dinesh Kumar, Kazuma Kobayashi, Shoaib Usman, Syed Alam
This chapter also focuses on the application of machine learning and artificial intelligence in the design optimization, control, and monitoring of small modular reactors.
no code implementations • 23 Mar 2021 • Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose, Mototaka Miyake, Masamichi Takahashi, Akiko Nakagawa, Tatsuya Harada, Ryuji Hamamoto
To support comparative diagnostic reading, content-based image retrieval (CBIR), which can selectively utilize normal and abnormal features in medical images as two separable semantic components, will be useful.
no code implementations • 12 Nov 2020 • Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose, Tatsuya Harada, Ryuji Hamamoto
Medical images can be decomposed into normal and abnormal features, which is considered as the compositionality.
1 code implementation • 26 May 2020 • Kazuma Kobayashi, Ryuichiro Hataya, Yusuke Kurose, Amina Bolatkan, Mototaka Miyake, Hirokazu Watanabe, Masamichi Takahashi, Jun Itami, Tatsuya Harada, Ryuji Hamamoto
In addition, we devise a metric to evaluate the anatomical fidelity of the reconstructed images and confirm that the overall detection performance is improved when the image reconstruction network achieves a higher score.