From the 1 of 8 linked papers with an AI index.
8 papers
ASTAR: Automated induction of STAndardized radiology Reporting templates from large-scale clinical free-text corpora
Xinfeng Zhang, Mingxuan Liu, Yifei Chen +9
Structured reporting converts free-text radiology narratives into queryable data keys, facilitating cohort assembly, longitudinal tracking, and training label generation for medica…
INFANiTE: Implicit Neural representation for high-resolution Fetal brain spatio-temporal Atlas learNing from clinical Thick-slicE MRI
Xiaotian Hu, Mingxuan Liu, Hongjia Yang +10
The paper introduces INFANiTE, an implicit neural representation framework that builds high‑resolution spatio‑temporal fetal brain atlases directly from thick‑slice MRI, eliminatin…
Annotation-free deep learning for detection and segmentation of fetal germinal matrix-intraventricular hemorrhage in brain MRI
Mingxuan Liu, Yingqi Hao, Yi Liao +17
Prenatal germinal matrix-intraventricular hemorrhage (GMH-IVH) is a leading cause of infant mortality and neurodevelopmental impairment, yet its manual diagnosis and lesion segment…
A physics-informed foundation model for quantitative diffusion MRI
Zihan Li, Jialan Zheng, Ziyu Li +18
Understanding the human brain requires access to its microscopic tissue architecture. Diffusion magnetic resonance imaging (MRI) provides the only noninvasive window into whole-bra…
CoilDrop-MRI: Self-supervised physics-guided MRI reconstruction with coil dropout
Tongxi Song, Ziyu Li, Zihan Li +6
Self-supervised deep learning-based methods have shown great promise for accelerated magnetic resonance imaging (MRI) reconstruction, achieving high image quality without requiring…
autoPET IV challenge: Incorporating organ supervision and human guidance for lesion segmentation in PET/CT
Junwei Huang, Yingqi Hao, Yitong Luo +6
Lesion Segmentation in PET/CT scans is an essential part of modern oncological workflows. To address the challenges of time-intensive manual annotation and high inter-observer vari…