| Presentation | 2021-06-03 A Study of Anomaly Detection for Renal Tubules by U-Net Emi Haneda, Yuya Honda, Hiroka Furuya, Satoshi Hara, Shigehiro Karashima, Hidetaka Nambo, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In this study, we applied deep learning segmentation to renal biopsy pathology specimen images and attempted to classify each tissue in the images. We focused on the tissue called anomaly tubules, which has not been classified in existing studies, and evaluated the accuracy of the classification. As a result, the accuracy of classification for normal and anomaly tubules was almost 90%, however, the accuracy for only anomaly tubules was about 40%. It requires the need for further improvement of the model and data collection. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Renal Biopsy / Segmentation / Anomaly Detection / Renal Tubules |
| Paper # | SIP2021-2,BioX2021-2,IE2021-2 |
| Date of Issue | 2021-05-27 (SIP, BioX, IE) |
| Conference Information | |
| Committee | IE / SIP / BioX / ITE-IST / ITE-ME |
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| Conference Date | 2021/6/3(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Hideaki Kimata(Kogakuin Univ.) / Kazunori Hayashi(Kyoto Univ.) / Akira Otsuka(AIST) / Junichi Akita(Kanazawa Univ.) / Hiroyuki Arai(Nippon Inst. of Tech.) |
| Vice Chair | Kazuya Kodama(NII) / Keita Takahashi(Nagoya Univ.) / Yukihiro Bandou(NTT) / Toshihisa Tanaka(Tokyo Univ. Agri.&Tech.) / Takahiro Aoki(Fujitsu Labs.) / Masatsugu Ichino(Univ. of Electro-Comm.) / Yutaka Hirose(Panasonic) |
| Secretary | Kazuya Kodama(KDDI Research) / Keita Takahashi(Nagoya Inst. of Tech.) / Yukihiro Bandou(Hosei Univ.) / Toshihisa Tanaka(Waseda Univ.) / Takahiro Aoki(SECOM) / Masatsugu Ichino(KDDI Research) / Yutaka Hirose(Saitama Univ.) |
| Assistant | Shunsuke Iwamura(NHK) / Shinobu Kudo(NTT) / Yuichi Tanaka(Tokyo Univ. Agri.&Tech.) / Emiko Sano(MitsubishiElectric) / Akihiro Hayasaka(NEC) |
| Paper Information | |
| Registration To | Technical Committee on Image Engineering / Technical Committee on Signal Processing / Technical Committee on Biometrics / Technical Group on Information Sensing Technologies / Technical Group on Media Engineering |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study of Anomaly Detection for Renal Tubules by U-Net |
| Sub Title (in English) | |
| Keyword(1) | Renal Biopsy |
| Keyword(2) | Segmentation |
| Keyword(3) | Anomaly Detection |
| Keyword(4) | Renal Tubules |
| 1st Author's Name | Emi Haneda |
| 1st Author's Affiliation | Kanazawa University(Kanazawa Univ.) |
| 2nd Author's Name | Yuya Honda |
| 2nd Author's Affiliation | Kanazawa University(Kanazawa Univ.) |
| 3rd Author's Name | Hiroka Furuya |
| 3rd Author's Affiliation | Kanazawa University(Kanazawa Univ.) |
| 4th Author's Name | Satoshi Hara |
| 4th Author's Affiliation | Kanazawa University(Kanazawa Univ.) |
| 5th Author's Name | Shigehiro Karashima |
| 5th Author's Affiliation | Kanazawa University(Kanazawa Univ.) |
| 6th Author's Name | Hidetaka Nambo |
| 6th Author's Affiliation | Kanazawa University(Kanazawa Univ.) |
| Date | 2021-06-03 |
| Paper # | SIP2021-2,BioX2021-2,IE2021-2 |
| Volume (vol) | vol.121 |
| Number (no) | SIP-54,BioX-55,IE-56 |
| Page | pp.pp.6-9(SIP), pp.6-9(BioX), pp.6-9(IE), |
| #Pages | 4 |
| Date of Issue | 2021-05-27 (SIP, BioX, IE) |