23 citations · 28 across the 7 of their papers we have counts for
7 papers
Open World MRI Reconstruction with Bias-Calibrated Adaptation
Jiyao Liu, Shangqi Gao, Lihao Liu +5
Real-world MRI reconstruction systems face the open-world challenge: test data from unseen imaging centers, anatomical structures, or acquisition protocols can differ drastically f…
Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification
Yibo Gao, Hangqi Zhou, Zheyao Gao +4
The pursuit of decision safety in clinical applications highlights the potential of concept-based methods in medical imaging. While these models offer active interpretability, they…
Empowering Medical Multi-Agents with Clinical Consultation Flow for Dynamic Diagnosis
Sihan Wang, Suiyang Jiang, Yibo Gao +3
Traditional AI-based healthcare systems often rely on single-modal data, limiting diagnostic accuracy due to incomplete information. However, recent advancements in foundation mode…
InDeed: Interpretable image deep decomposition with guaranteed generalizability
Sihan Wang, Shangqi Gao, Fuping Wu +1
Image decomposition aims to analyze an image into elementary components, which is essential for numerous downstream tasks and also by nature provides certain interpretability to th…
Multi-modal MRI Translation via Evidential Regression and Distribution Calibration
Jiyao Liu, Shangqi Gao, Yuxin Li +9
Multi-modal Magnetic Resonance Imaging (MRI) translation leverages information from source MRI sequences to generate target modalities, enabling comprehensive diagnosis while overc…
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
Kelly Payette, Céline Steger, Roxane Licandro +64
Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and th…
BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability
Shangqi Gao, Hangqi Zhou, Yibo Gao +1
Due to the cross-domain distribution shift aroused from diverse medical imaging systems, many deep learning segmentation methods fail to perform well on unseen data, which limits t…