| Presentation | 2020-11-04 Performance of automated melanoma diagnosis by adding lesion information to deep convolutional neural network Ginji Hirano, Mitsutaka Nemoto, Yuich Kimura, Takashi Nagaoka, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Melanoma is a type of superficial tumor, which is highly malignant. Early-stage melanoma is difficult to diagnose because it looks like a benign lesion. In this study, we developed an automatic melanoma diagnostic system using the DCNN. We replaced any one of the three channels of the RGB image with the lesion image to pay attention to the lesion in the DCNN. In this study, we show the analysis results using 1000 cases of melanoma and non-melanoma. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | melanoma / Deep Learngin / VGG16 / HAM10000 |
| Paper # | MICT2020-20,MI2020-46 |
| Date of Issue | 2020-10-28 (MICT, MI) |
| Conference Information | |
| Committee | MICT / MI |
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| Conference Date | 2020/11/4(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Virtual (TBD) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Medical imaging technology, healthcare and medical information communication technology |
| Chair | Eisuke Hanada(Saga Univ.) / Yoshiki Kawata(Tokushima Univ.) |
| Vice Chair | Hirokazu Tanaka(Hiroshima City Univ.) / Daisuke Anzai(Nagoya Inst. of Tech.) / Takayuki Kitasaka(Aichi Inst. of Tech.) / Hidekata Hontani(Nagoya Inst. of Tech.) |
| Secretary | Hirokazu Tanaka(Kobe Univ.) / Daisuke Anzai(Yokohama National Univ.) / Takayuki Kitasaka(Yamaguchi Univ.) / Hidekata Hontani(Univ. of Hyogo) |
| Assistant | Keita Saku(Kyushu Univ.) / Kai Ishida(KISTEC) / Kento Takabayashi(Okayama Pref. Univ.) / Hotaka Takizawa(Tsukuba Univ.) / Yoshito Otake(NAIST) |
| Paper Information | |
| Registration To | Technical Committee on Healthcare and Medical Information Communication Technology / Technical Committee on Medical Imaging |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Performance of automated melanoma diagnosis by adding lesion information to deep convolutional neural network |
| Sub Title (in English) | * |
| Keyword(1) | melanoma |
| Keyword(2) | Deep Learngin |
| Keyword(3) | VGG16 |
| Keyword(4) | HAM10000 |
| 1st Author's Name | Ginji Hirano |
| 1st Author's Affiliation | Kindai University(Kindai University) |
| 2nd Author's Name | Mitsutaka Nemoto |
| 2nd Author's Affiliation | Kindai University(Kindai University) |
| 3rd Author's Name | Yuich Kimura |
| 3rd Author's Affiliation | Kindai University(Kindai University) |
| 4th Author's Name | Takashi Nagaoka |
| 4th Author's Affiliation | Kindai University(Kindai University) |
| Date | 2020-11-04 |
| Paper # | MICT2020-20,MI2020-46 |
| Volume (vol) | vol.120 |
| Number (no) | MICT-219,MI-220 |
| Page | pp.pp.62-64(MICT), pp.62-64(MI), |
| #Pages | 3 |
| Date of Issue | 2020-10-28 (MICT, MI) |