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ShanghaiTech University Knowledge Management System
Multimodal Local Representation Learning For Multi-Task Blastocyst Assessment | |
2024-10 | |
会议录名称 | IEEE SYMPOSIUM ON BIOMEDICAL IMAGING 2024 |
ISSN | 1945-7928 |
发表状态 | 已发表 |
DOI | 10.1109/ISBI56570.2024.10635863 |
摘要 | Blastocyst assessment is a critical step to influence the live birth rate in the in vitro fertilization (IVF) treatment. We propose a pioneer multimodal local representation learning framework that leverages both visual and textual information, which provides a comprehensive and automatic assessment of blastocyst quality. The model redefines the blastocyst assessment as an image-text retrieval multi-task, assessing two main blastocyst components, the inner cell mass (ICM) and trophoblast (TE), respectively. By learning local representation, our approach captures the fine-grained similarity between text descriptions and image patches, enhancing the accuracy and interpretability of the assessment model. The experimental results are promising, achieving accuracy 89.1% for ICM and 91.6% for TE respectively. Furthermore, this proposed local representation learning framework may extend to other multi-task biomedical imaging applications. |
会议录编者/会议主办者 | AI2D Center ; et al. ; Therapanacea ; Thermo Fisher Scientific ; United Imaging Intelligence ; Verasonics |
关键词 | Adversarial machine learning Cell culture Contrastive Learning Image representation Image retrieval Multi-task learning Biomedical images Blastocyst assessment Image texts Image-text retrieval Learning frameworks Multi tasks Multi-modal Multi-task model Multimodal local representation Text retrieval |
会议名称 | 21st IEEE International Symposium on Biomedical Imaging, ISBI 2024 |
会议地点 | Athens, Greece |
会议日期 | 27-30 May 2024 |
URL | 查看原文 |
收录类别 | EI |
语种 | 英语 |
出版者 | IEEE Computer Society |
EI入藏号 | 20243717024919 |
EI主题词 | Medical imaging |
EISSN | 1945-8452 |
EI分类号 | 101.1 ; 101.3 ; 101.7 ; 102.2.1 ; 1101.2 ; 1106.3.1 ; 746 Imaging Techniques |
原始文献类型 | Conference article (CA) |
来源库 | IEEE |
文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/378350 |
专题 | 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)_杨锐组 创意与艺术学院_PI研究组(P)_武颖娜组 |
共同第一作者 | Zhang J(张军); Xie GP(谢广平) |
通讯作者 | Ni N(倪娜) |
作者单位 | 1.上海科技大学 2.西安交通大学第一附属医院 |
第一作者单位 | 上海科技大学 |
通讯作者单位 | 上海科技大学 |
第一作者的第一单位 | 上海科技大学 |
推荐引用方式 GB/T 7714 | Zhang J,Zheng BZ,Ni N,et al. Multimodal Local Representation Learning For Multi-Task Blastocyst Assessment[C]//AI2D Center, et al., Therapanacea, Thermo Fisher Scientific, United Imaging Intelligence, Verasonics:IEEE Computer Society,2024. |
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