计算机科学
序列(生物学)
人工智能
公制(单位)
代表(政治)
模式识别(心理学)
排名(信息检索)
源代码
深度学习
编码(集合论)
秩(图论)
数学
生物
集合(抽象数据类型)
运营管理
法学
程序设计语言
经济
遗传学
政治学
组合数学
操作系统
政治
作者
Luyi Han,Tao Tan,Tianyu Zhang,Yunzhi Huang,Xin Wang,Yuan Gao,Jonas Teuwen,Ritse M. Mann
标识
DOI:10.1016/j.media.2023.103044
摘要
Multi-sequence MRIs can be necessary for reliable diagnosis in clinical practice due to the complimentary information within sequences. However, redundant information exists across sequences, which interferes with mining efficient representations by learning-based models. To handle various clinical scenarios, we propose a sequence-to-sequence generation framework (Seq2Seq) for imaging-differentiation representation learning. In this study, not only do we propose arbitrary 3D/4D sequence generation within one model to generate any specified target sequence, but also we are able to rank the importance of each sequence based on a new metric estimating the difficulty of a sequence being generated. Furthermore, we also exploit the generation inability of the model to extract regions that contain unique information for each sequence. We conduct extensive experiments using three datasets including a toy dataset of 20,000 simulated subjects, a brain MRI dataset of 1,251 subjects, and a breast MRI dataset of 2,101 subjects, to demonstrate that (1) top-ranking sequences can be used to replace complete sequences with non-inferior performance; (2) combining MRI with our imaging-differentiation map leads to better performance in clinical tasks such as glioblastoma MGMT promoter methylation status prediction and breast cancer pathological complete response status prediction. Our code is available at https://github.com/fiy2W/mri_seq2seq.
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