| Paper Abstract and Keywords |
| Presentation |
2021-02-18 13:50
Detecting axillary lymph node metastasis of breast cancer with FDG-PET/CT images based on attention mechanism
Zongyao Li, Ren Togo, Kenji Hirata (Hokkaido Univ.), Kazuhiro Kitajima (Hyogo Med.), Junki Takenaka (Hokkaido Univ.), Yasuo Miyoshi (Hyogo Med.), Kohsuke Kudo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
Determination of axillary nodal status is significant to treatment of breast cancer. Typically, the diagnosis of axillary lymph node (LN) metastasis is performed by using invasive methods which impose considerable burden on patients. On the other hand, noninvasive FDG-PET/CT can be also used for diagnosing axillary LN metastasis but has inferior performance. In this paper, we focus on detecting axillary LN metastasis of breast cancer with FDG-PET/CT images by using convolutional neural networks (CNNs). Specifically, we equip a 3D residual CNN with an attention mechanism. The attention mechanism can make the network focus on the most meaningful regions related to the diagnosis. As a result, the diagnostic performance is considerably improved by the attention mechanism compared to a simple CNN. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
breast cancer / axillary lymph node / FDG-PET/CT / convolutional neural network / / / / |
| Reference Info. |
ITE Tech. Rep., vol. 45, no. 4, ME2021-7, pp. 33-36, Feb. 2021. |
| Paper # |
ME2021-7 |
| Date of Issue |
2021-02-11 (MMS, ME, AIT) |
| ISSN |
Print edition: ISSN 1342-6893 Online edition: ISSN 2424-1970 |
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