9 papers
Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification
Jyun-Ping Kao, Jiaxin Yang, C. -C. Jay Kuo +1
Echocardiography is a cornerstone for managing heart failure (HF), with Left Ventricular Ejection Fraction (LVEF) being a critical metric for guiding therapy. However, manual LVEF…
Speech-Guided Multimodal Learning for Vocal Tract Segmentation in Real-Time MRI
Daiqi Liu, Lukas Mulzer, Md Hasan +11
Segmenting vocal tract articulators in real-time MRI (rtMRI) is a challenging dynamic image segmentation problem characterized by low contrast, rapid motion, and limited spatial re…
SIREM: Speech-Informed MRI Reconstruction with Learned Sampling
Md Hasan, Nyvenn Castro, Daiqi Liu +6
Real-time magnetic resonance imaging (rtMRI) of speech production enables non-invasive visualization of dynamic vocal-tract motion and is valuable for speech science and clinical a…
VocSegMRI: Multimodal Learning for Precise Vocal Tract Segmentation in Real-time MRI
Daiqi Liu, Johannes Enk, Maureen Stone +7
Accurate segmentation of articulatory structures in real-time MRI (rtMRI) remains challenging, as existing methods rely primarily on visual cues and overlook complementary informat…
Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation
Jyun-Ping Kao, Shinyeong Rho, Shahar Lazarev +5
Early diagnosis of attention-deficit/hyperactivity disorder (ADHD) in children plays a crucial role in improving outcomes in education and mental health. Diagnosing ADHD using neur…
Principled Feature Disentanglement for High-Fidelity Unified Brain MRI Synthesis
Jihoon Cho, Jonghye Woo, Jinah Park
Multisequence Magnetic Resonance Imaging (MRI) provides a more reliable diagnosis in clinical applications through complementary information across sequences. However, in practice,…