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Technical Committee on Medical Imaging (MI) (Searched in: 2019)
Search Results: Keywords 'from:2019-09-04 to:2019-09-04'
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-04 14:10 |
Okayama | [more] | |||
| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-04 15:40 |
Okayama |  PRMU2019-12 MI2019-31 |
(To be available after the conference date) [more] | PRMU2019-12 MI2019-31 pp.3-7 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-04 15:55 |
Okayama | PRMU2019-13 MI2019-32 |
(To be available after the conference date) [more] | PRMU2019-13 MI2019-32 pp.9-13 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-04 16:10 |
Okayama | [Short Paper]
Quantitative evaluation of lymph nodes in colorectal cancer using 3-phase abdominal CT images Ren Nishimoto, Hidenobu Suzuki, Yoshiki Kawata, Noboru Niki (Tokushima Univ.), Gen Iinuma (NCC) PRMU2019-14 MI2019-33 |
“Presence / absence of lymph node metastasis” is an important indicator in staging of colorectal cancer. In this study, ... [more] | PRMU2019-14 MI2019-33 pp.15-18 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-04 16:20 |
Okayama | Domain-adversarial multiple instance learning for subtype classification of malignant lymphoma Daisuke Fukushima, Ryoichi Koga, Noriaki Hashimoto, Kaho Ko (Nagoya Inst. of Tech), Masato Nakaguro, Kei Kohno, Shigeo Nakamura (Nagoya Univ. Hospital), Hidekata Hontani (Nagoya Inst of Tech), Ichiro Takeuchi (Nagoya Inst. of Tech/RIKEN/NIMS) PRMU2019-15 MI2019-34 |
We classify subtypes of malignant lymphoma using convolutional neural network with digital pathological images as input ... [more] | PRMU2019-15 MI2019-34 pp.19-24 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-04 15:40 |
Okayama | Preliminary investigation on decomposition of individual muscles and bones of lower extremity from single radiograph using CycleGAN Naoki Nakanishi, Yuta Hiasa, Yoshito Otake (NAIST), Masaki Takao, Nobuhiko Sugano (Osaka Univ.), Yoshinobu Sato (NAIST) PRMU2019-16 MI2019-35 |
Extraction of musculoskeletal structures of lower extremity from medical images is useful for quantitatively understandi... [more] | PRMU2019-16 MI2019-35 pp.25-30 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-04 15:55 |
Okayama | Analysis of pneumothorax deformation for in vivo animal lungs using model-based registration Kotaro Kobayashi, Megumi Nakao (Kyoto Univ.), Junko Tokuno, Toyofumi F. Chen-Yoshikawa, Hiroshi Date (Kyoto Univ. Hospital), Tetsuya Matsuda (Kyoto Univ.) PRMU2019-17 MI2019-36 |
[more] | PRMU2019-17 MI2019-36 pp.31-36 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-04 16:10 |
Okayama | PRMU2019-18 MI2019-37 |
This report discusses improvement of the recognition rate of our face recognition system based on photometric adjustment... [more] | PRMU2019-18 MI2019-37 pp.37-42 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-04 16:25 |
Okayama | Inter-frame interpolation by height constraint for sequential human silhouette images
-- Application to forensic gait analysis under low frame-rate condition -- Daisuke Imoto, Kenji Kurosawa, Masakatsu Honma, Ryo Yokota, Manato Hirabayashi, Yoshinori Hawai (NRIPS) PRMU2019-19 MI2019-38 |
Boundary information is important for defining the shape of an object or a person as a first approximation, and widely u... [more] | PRMU2019-19 MI2019-38 pp.43-48 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-04 16:40 |
Okayama | PRMU2019-20 MI2019-39 |
(To be available after the conference date) [more] | PRMU2019-20 MI2019-39 pp.49-52 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-05 10:00 |
Okayama | [Short Paper]
Soft-constrained clustering for facilitating image labeling Ryoma Bise, Kentaro Abe, Hideaki Hayashi (Kyushu Univ.), Kiyohito Tanaka (Kyoto Second Red Cross Hospital), Seiichi Uchida (Kyushu Univ.) PRMU2019-21 MI2019-40 |
(To be available after the conference date) [more] | PRMU2019-21 MI2019-40 pp.53-56 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-05 10:10 |
Okayama | PRMU2019-22 MI2019-41 |
(To be available after the conference date) [more] | PRMU2019-22 MI2019-41 pp.57-61 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-05 10:20 |
Okayama | Metal artifact reduction using CycleGAN for CT images Megumi Nakao (Kyoto Univ.), Kieho Imanishi (e-Grwoth), Nobuhiro Ueda (Nara Med.), Yuichiro Imai (Otowa Hosp.), Tadaaki Kirita (Nara Med.), Tetsuya Matsuda (Kyoto Univ.) PRMU2019-23 MI2019-42 |
(To be available after the conference date) [more] | PRMU2019-23 MI2019-42 pp.63-68 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-05 10:35 |
Okayama | [Short Paper]
Dynamic PET Image Reconstruction using Non-Negative Matrix Decomposition with Deep Image Prior Tomoshige Shimomura, Kazuya Kawai (NIT), Muneyuki Sakata (Tokyo Metro. Inst. Gerontology), Yuichi Kimura (KU), Tatsuya Yokota, Hidekata Hontani (NIT) PRMU2019-24 MI2019-43 |
We present a PET image reconstruction method that can reconstruct dynamic PET images with high SN ratio and can simultan... [more] | PRMU2019-24 MI2019-43 pp.69-70 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-05 13:40 |
Okayama | A method for visualizing the cause of misrecognition in object recognition using CNN Tomonori Kubota (Fujitsu Lab.), Yasuyuki Murata (FST), Yoshifumi Uehara, Akira Nakagawa (Fujitsu Lab.) PRMU2019-25 MI2019-44 |
In this paper, we propose a method for visualizing the cause of misrecognition in object recognition using CNN. By this ... [more] | PRMU2019-25 MI2019-44 pp.99-104 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-05 13:55 |
Okayama | Hierarchical Classification to Detect Type of Diseases and Abnormality Simultaneously in Optical Coherence Tomography Images Yudai Kato, Yuji Ayatsuka, Takaki Uta (CRESCO), Soichiro Kuwayama, Hideaki Usui, Aki Kato, Yuichiro Ogura, Tsutomu Yasukawa (Nagoya City University) PRMU2019-26 MI2019-45 |
Analyzing medical images with machine learning is useful not only for classifying types of diseases but for screening ... [more] |
PRMU2019-26 MI2019-45 pp.105-108 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-05 14:10 |
Okayama | Rough domain adaptation through model selection for neural networks Azusa Sawada, Takashi Shibata, Shoji Yachida (NEC) PRMU2019-27 MI2019-46 |
[more] | PRMU2019-27 MI2019-46 pp.109-113 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-05 14:25 |
Okayama | Active learning based on self-supervised feature learning Shunsuke Tsukatani, Kazuhiko Murasaki, Shingo Andou, jun Shimamura (NTT) PRMU2019-28 MI2019-47 |
In this paper, we propose an active learning algorithm based on self-supervised feature learning. When the domain of the... [more] | PRMU2019-28 MI2019-47 pp.115-119 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-05 14:10 |
Okayama | Analysis and Feature Selection of CNN Features
-- Recognition of Neoplasia by using Endocytoscopic Images -- Hayato Itoh (Nagoya Univ.), Yuichi Mori, Masashi Misawa (Showa Univ.), Masahiro Oda (Nagoya Univ.), Shin-Ei Kudo (Showa Univ.), Kensaku Mori (Nagoya Univ.) PRMU2019-29 MI2019-48 |
Pathological pattern classification is based on texture patterns in ultra magnified view of polyp surfaces. Deep learni... [more] |
PRMU2019-29 MI2019-48 pp.129-134 |
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| PRMU, MI, IPSJ-CVIM [detail] | 2019-09-05 14:25 |
Okayama | Mixed Domain Learning for Computer-aided Diagnosis of Endoscopy Images in Deep Convolutional Neural Networks Yuta Kochi (Tsukuba Univ./AIST), Hirokazu Nosato, Masahiro Murakawa, Hidenori Sakanashi (AIST) PRMU2019-30 MI2019-49 |
(To be available after the conference date) [more] | PRMU2019-30 MI2019-49 pp.135-138 |
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