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454 results on '"Tatsuya Harada"'

1. The Sound Demixing Challenge 2023 – Music Demixing Track

Author

Giorgio Fabbro, Stefan Uhlich, Chieh-Hsin Lai, Woosung Choi, Marco Martínez-Ramírez, Weihsiang Liao, Igor Gadelha, Geraldo Ramos, Eddie Hsu, Hugo Rodrigues, Fabian-Robert Stöter, Alexandre Défossez, Yi Luo, Jianwei Yu, Dipam Chakraborty, Sharada Mohanty, Roman Solovyev, Alexander Stempkovskiy, Tatiana Habruseva, Nabarun Goswami, Tatsuya Harada, Minseok Kim, Jun Hyung Lee, Yuanliang Dong, Xinran Zhang, Jiafeng Liu, and Yuki Mitsufuji

Subjects
music source separation, deep learning, neural networks, robust training, sound, signal processing, Information technology, T58.5-58.64, Music, M1-5000
Abstract

This paper summarizes the music demixing (MDX) track of the Sound Demixing Challenge (SDX’23). We provide a summary of the challenge setup and introduce the task of robust music source separation (MSS), i.e., training MSS models in the presence of errors in the training data. We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding.1 We describe the methods that achieved the highest scores in the competition. Moreover, we present a direct comparison with the previous edition of the challenge (the Music Demixing Challenge 2021): the best performing system achieved an improvement of over 1.6dB in signal-to-distortion ratio over the winner of the previous competition, when evaluated on MDXDB21. Besides relying on the signal-to-distortion ratio as objective metric, we also performed a listening test with renowned producers and musicians to study the perceptual quality of the systems and report here the results. Finally, we provide our insights into the organization of the competition and our prospects for future editions.

Published
2024
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2. Improved Object Detection Artificial Intelligence Using the Revised RetinaNet Model for the Automatic Detection of Ulcerations, Vascular Lesions, and Tumors in Wireless Capsule Endoscopy

Author

Ayako Nakada, Ryota Niikura, Keita Otani, Yusuke Kurose, Yoshito Hayashi, Kazuya Kitamura, Hiroyoshi Nakanishi, Seiji Kawano, Testuya Honda, Kenkei Hasatani, Tetsuya Sumiyoshi, Tsutomu Nishida, Atsuo Yamada, Tomonori Aoki, Tatsuya Harada, Takashi Kawai, and Mitsuhiro Fujishiro

Subjects
artificial intelligence, small intestine erosions and ulcers, small intestine tumors, small intestine vascular lesions, wireless capsule endoscopy, Biology (General), QH301-705.5
Abstract

The use of computer-aided detection models to diagnose lesions in images from wireless capsule endoscopy (WCE) is a topical endoscopic diagnostic solution. We revised our artificial intelligence (AI) model, RetinaNet, to better diagnose multiple types of lesions, including erosions and ulcers, vascular lesions, and tumors. RetinaNet was trained using the data of 1234 patients, consisting of images of 6476 erosions and ulcers, 1916 vascular lesions, 7127 tumors, and 14,014,149 normal tissues. The mean area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for each lesion were evaluated using five-fold stratified cross-validation. Each cross-validation set consisted of between 6,647,148 and 7,267,813 images from 217 patients. The mean AUC values were 0.997 for erosions and ulcers, 0.998 for vascular lesions, and 0.998 for tumors. The mean sensitivities were 0.919, 0.878, and 0.876, respectively. The mean specificities were 0.936, 0.969, and 0.937, and the mean accuracies were 0.930, 0.962, and 0.924, respectively. We developed a new version of an AI-based diagnostic model for the multiclass identification of small bowel lesions in WCE images to help endoscopists appropriately diagnose small intestine diseases in daily clinical practice.

Published
2023
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3. Random‐start ovarian stimulation with aromatase inhibitor for fertility preservation in women with Japanese breast cancer

Author

Takashi Nakasuji, Kiyotaka Kawai, Tomonori Ishikawa, Kaori Teraoka, Shiho Takeuchi, Tomoko Miyagawa, Kazuko Nara, Nobuyuki Kidera, Tatsuya Harada, and Naoyuki Miyasaka

Subjects
aromatase inhibitor, breast cancer, double ovarian stimulation, fertility preservation, random-start, Diseases of the endocrine glands. Clinical endocrinology, RC648-665, Reproduction, QH471-489
Abstract

Abstract Purpose Fertility preservation is an important issue for young cancer patients. Random‐start controlled ovarian stimulation and double ovarian stimulation have been proposed for efficient oocyte retrieval within the limited time before cancer therapy. We aimed to clarify the efficacy of these new protocols within the Japanese population. Methods We performed a retrospective observational study at a multicenter from February 2012 to August 2017. The study entailed 50 cycles with 34 patients who underwent fertility preservation due to breast cancer. Follicular phase or luteal phase ovarian stimulation with aromatase inhibitor was performed. A second ovarian stimulation was started with or without waiting until the next menstruation. We measured the number of retrieved oocytes and cryopreserved oocytes/embryos, the ratio of mature oocytes, and the fertilization rate. Results The numbers of retrieved oocytes and frozen oocytes/embryos were not significantly different between follicular phase and luteal phase ovarian stimulation. The number of retrieved oocytes was not reduced at the second ovum pick up compared to the first ovum pick up in the double ovarian stimulation. Conclusions Random‐start controlled ovarian stimulation and double ovarian stimulation with aromatase inhibitor for breast cancer patients were effective protocols for retrieving a greater number of oocytes within the limited time.

Published
2019
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4. Guideline for cryopreservation of unfertilized eggs and ovarian tissues in Japan Society of Reproductive Medicine

Author

Tasuku Harada, Naoaki Kuji, Osamu Ishihara, Tomohiko Ichikawa, Minoru Irahara, Yukiko Katagiri, Hidekazu Saito, Tatsuya Harada, Osamu Wada‐Hiraike, Fuminori Taniguchi, and Ethics Committee in Japan Society of Reproductive Medicine

Subjects
Diseases of the endocrine glands. Clinical endocrinology, RC648-665, Reproduction, QH471-489
Published
2019
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5. Semi-Supervised Learning in Medical Images Through Graph-Embedded Random Forest

Author

Lin Gu, Xiaowei Zhang, Shaodi You, Shen Zhao, Zhenzhong Liu, and Tatsuya Harada

Subjects
vessel segmentation, semi-supervised learning, manifold learning, central nervous system (CNS), retinal image, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

One major challenge in medical imaging analysis is the lack of label and annotation which usually requires medical knowledge and training. This issue is particularly serious in the brain image analysis such as the analysis of retinal vasculature, which directly reflects the vascular condition of Central Nervous System (CNS). In this paper, we present a novel semi-supervised learning algorithm to boost the performance of random forest under limited labeled data by exploiting the local structure of unlabeled data. We identify the key bottleneck of random forest to be the information gain calculation and replace it with a graph-embedded entropy which is more reliable for insufficient labeled data scenario. By properly modifying the training process of standard random forest, our algorithm significantly improves the performance while preserving the virtue of random forest such as low computational burden and robustness over over-fitting. Our method has shown a superior performance on both medical imaging analysis and machine learning benchmarks.

Published
2020
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6. Parental age and gene expression profiles in individual human blastocysts

Author

Kiyotaka Kawai, Tatsuya Harada, Tomonori Ishikawa, Rikikazu Sugiyama, Toshihiro Kawamura, Atsumi Yoshida, Osamu Tsutsumi, Fumitoshi Ishino, Toshiro Kubota, and Takashi Kohda

Subjects
Medicine, Science
Abstract

Abstract The epigenetic status of the genome changes dynamically from fertilization to implantation. In addition, the physiological environment during the process of gametogenesis, including parental age, may affect the epigenome of the embryo after fertilization. It is important to clarify the influence of parental age on gene expression in the embryo in terms of transgenerational epigenetics to improve the techniques currently used in assisted reproductive medicine. Here, we performed single-embryo RNA-seq analysis on human blastocysts fertilized by intracytoplasmic sperm injection, including from relatively elderly mothers, to elucidate the effects of parental age on the embryonic gene expression profile. We identified a number of genes in which the expression levels were decreased with increasing maternal age. Among these genes, several are considered to be important for meiotic chromosomal segregation, such as PTTG1, AURKC, SMC1B and MEIKIN. Furthermore, the expression levels of certain genes critical for autophagy and embryonic growth, specifically GABARAPL1 and GABARAPL3, were negatively correlated with advanced paternal age. In addition, levels of transcripts derived from major satellite repeats also decreased as the maternal age increased. These results suggest that epigenetic modifications of the oocyte genome may change with parental age and be transmitted to the next generation.

Published
2018
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10. Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration.

Author

Abby O'Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta 0004, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alexander Herzog, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Beomjoon Kim, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu 0003, Charlotte Le, Chelsea Finn, Chen Wang 0053, Chenfeng Xu, Cheng Chi, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Driess, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Ge Yan, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su 0001, Haoshu Fang, Haochen Shi, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim 0001, Jaimyn Drake, Jan Peters 0001, Jan Schneider, Jasmine Hsu, Jeannette Bohg, Jeffrey Bingham, Jeffrey Wu, Jensen Gao, Jiaheng Hu, Jiajun Wu 0001, Jialin Wu, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jingyun Yang, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kaiyuan Wang, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Ken Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Lin, Kevin Zhang 0002, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Lawrence Yunliang Chen, Lerrel Pinto, Li Fei-Fei 0001, Liam Tan, Linxi Jim Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma 0001, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen 0001, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Ning Liu, Norman Di Palo, Nur Muhammad (Mahi) Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Quan Vuong, Rafael Rafailov, Ran Tian, Ria Doshi, Roberto Martín-Martín, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Ruohan Zhang, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante, Sean Kirmani, Sergey Levine, Shan Lin, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair 0003, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wei Zhan, Wenxuan Zhou, Wolfram Burgard, Xi Chen, Xiaolong Wang, Xinghao Zhu, Xinyang Geng, Xiyuan Liu, Liangwei Xu, Xuanlin Li, Yao Lu, Yecheng Jason Ma, Yejin Kim, Yevgen Chebotar, Yifan Zhou, Yifeng Zhu, Yilin Wu, Ying Xu, Yixuan Wang, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yue Cao, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang 0001, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zhuo Xu, Zichen Jeff Cui, Zichen Zhang 0016, and Zipeng Lin

Published
2024
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