ShanghaiTech University Knowledge Management System
| Multimodal Representation Learning for Blastocyst Assessment | |
| 2023 | |
| 会议录名称 | IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING |
| ISSN | 1945-7928 |
| 卷号 | 2023-April |
| 发表状态 | 已发表 |
| DOI | 10.1109/ISBI53787.2023.10230468 |
| 摘要 | Blastocyst selection based on morphology grading is crucial in in vitro fertilization (IVF) treatment. Several research studies based on convolutional neural networks (CNNs) have been reported to select the most viable blastocyst automatically. In this paper, we propose a multimodal representation learning framework in which the text description is firstly streamed as a complementary supervision signal to enrich the visual information. Moreover, we redefine the blastocyst assessment problem to an image-text retrieval task to solve the data imbalance. The experimental results show that the performance metrics, e.g., accuracy, outperform the unimodal classification (+1.5%) and image retrieval counterparts (+1.2%), which demonstrates our proposed model's effectiveness. © 2023 IEEE. |
| 会议录编者/会议主办者 | Flywheel ; Kitware ; Siemens Healthineers ; UCLouvain |
| 关键词 | Blastocyst Assessment Multimodal Representation Learning Image-text Retrieval Visual Transformer |
| 会议名称 | 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 |
| 出版地 | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| 会议地点 | Cartagena, Colombia |
| 会议日期 | 18-21 April 2023 |
| URL | 查看原文 |
| 收录类别 | EI ; CPCI-S |
| 语种 | 英语 |
| WOS研究方向 | Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging |
| WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging |
| WOS记录号 | WOS:001062050500146 |
| 出版者 | IEEE Computer Society |
| EI入藏号 | 20233914806266 |
| EI主题词 | Grading |
| EISSN | 1945-8452 |
| EI分类号 | 723.5 Computer Applications ; 741.2 Vision |
| 原始文献类型 | Conference article (CA) |
| 来源库 | IEEE |
| 引用统计 | 正在获取...
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| 文献类型 | 会议论文 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/305053 |
| 专题 | 创意与艺术学院 信息科学与技术学院_硕士生 创意与艺术学院_PI研究组(P)_谢广平组 创意与艺术学院_PI研究组(P)_武颖娜组 |
| 通讯作者 | Ni, Na; Tong, Guoqing |
| 作者单位 | 1.ShanghaiTech Univ, Sch Creat & Art, Ctr Adapt Syst Engn, Shanghai, Peoples R China 2.Xi An Jiao Tong Univ, Dept Reprod Med, Affiliated Hosp 1, Xian, Peoples R China 3.Shuguang Hosp, Reprod Med Ctr, Shanghai, Peoples R China |
| 第一作者单位 | 创意与艺术学院 |
| 通讯作者单位 | 创意与艺术学院 |
| 第一作者的第一单位 | 创意与艺术学院 |
| 推荐引用方式 GB/T 7714 | Wang, Youcheng,Zheng, Zhe,Ni, Na,et al. Multimodal Representation Learning for Blastocyst Assessment[C]//Flywheel, Kitware, Siemens Healthineers, UCLouvain. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE Computer Society,2023. |
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