4 citations · 12 across the 12 of their papers we have counts for
12 papers
Free-RBF-KAN: Kolmogorov-Arnold Networks with Adaptive Radial Basis Functions for Efficient Function Learning
Shao-Ting Chiu, Siu Wun Cheung, Ulisses Braga-Neto +2
Kolmogorov-Arnold Networks (KANs) offer a promising framework for approximating complex nonlinear functions, yet the original B-spline formulation suffers from significant computat…
LGBP-OrgaNet: Learnable Gaussian Band Pass Fusion of CNN and Transformer Features for Robust Organoid Segmentation and Tracking
Jing Zhang, Siying Tao, Jiao Li +6
Organoids replicate organ structure and function, playing a crucial role in fields such as tumor treatment and drug screening. Their shape and size can indicate their developmental…
IsoNet: Causal Analysis of Multimodal Transformers for Neuromuscular Gesture Classification
Eion Tyacke, Kunal Gupta, Jay Patel +1
Hand gestures are a primary output of the human motor system, yet the decoding of their neuromuscular signatures remains a bottleneck for basic neuroscience and assistive technolog…
DeFusion: An Effective Decoupling Fusion Network for Multi-Modal Pregnancy Prediction
Xueqiang Ouyang, Jia Wei, Wenjie Huo +3
Temporal embryo images and parental fertility table indicators are both valuable for pregnancy prediction in \textbf{in vitro fertilization embryo transfer} (IVF-ET). However, curr…
Unsupervised Tumor-Aware Distillation for Multi-Modal Brain Image Translation
Chuan Huang, Jia Wei, Rui Li
Multi-modal brain images from MRI scans are widely used in clinical diagnosis to provide complementary information from different modalities. However, obtaining fully paired multi-…
Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learning
Zecheng Liu, Jia Wei, Rui Li
Due to the difficulties of obtaining multimodal paired images in clinical practice, recent studies propose to train brain tumor segmentation models with unpaired images and capture…