| Presentation | 2020-03-11 Accuracy of Brain Tumor Detection and Classification Based on Under Sampled k-Space Signals Tania Sultana, Sho Kurosaki, Yutaka Jitsumatsu, Junichi Takeuchi, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The prime concern of Magnetic Resonance Imaging (MRI) is to optimizeexamination time by assuring a good quality of the images. In thisaspect, a newly developed deep learning method, called multi-resolution CNN (MRCNN), was proposed by Kitazakiet al. The key focus of MRCNN is that, it can restore high quality image fromunder sampled $k$-space signals. Kitazaki et al. evaluated its performance in term of Peak Signal to Noise Ratio(PSNR). The aim of this study is to evaluate the performance of MRCNNin the field of brain tumor detection and classification based ontransfer learning. This paper highlights the accuracy of detectionand classification using mRCNN is significantly higher in contrastwithout MRCNN. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | MRI reconstructionunder sampled k-space signalstransfer learning |
| Paper # | IBISML2019-46 |
| Date of Issue | 2020-03-03 (IBISML) |
| Conference Information | |
| Committee | IBISML |
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| Conference Date | 2020/3/10(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kyoto University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Machine learning, etc. |
| Chair | Hisashi Kashima(Kyoto Univ.) |
| Vice Chair | Masashi Sugiyama(Univ. of Tokyo) / Koji Tsuda(Univ. of Tokyo) |
| Secretary | Masashi Sugiyama(Nagoya Inst. of Tech.) / Koji Tsuda(AIST) |
| Assistant | Tomoharu Iwata(NTT) / Shigeyuki Oba(Kyoto Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Infomation-Based Induction Sciences and Machine Learning |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Accuracy of Brain Tumor Detection and Classification Based on Under Sampled k-Space Signals |
| Sub Title (in English) | |
| Keyword(1) | MRI reconstructionunder sampled k-space signalstransfer learning |
| 1st Author's Name | Tania Sultana |
| 1st Author's Affiliation | Kyushu University(Kyushu Univ.) |
| 2nd Author's Name | Sho Kurosaki |
| 2nd Author's Affiliation | Kyushu University(Kyushu Univ.) |
| 3rd Author's Name | Yutaka Jitsumatsu |
| 3rd Author's Affiliation | Kyushu University(Kyushu Univ.) |
| 4th Author's Name | Junichi Takeuchi |
| 4th Author's Affiliation | Kyushu University(Kyushu Univ.) |
| Date | 2020-03-11 |
| Paper # | IBISML2019-46 |
| Volume (vol) | vol.119 |
| Number (no) | IBISML-476 |
| Page | pp.pp.91-94(IBISML), |
| #Pages | 4 |
| Date of Issue | 2020-03-03 (IBISML) |