4 citations · 8 across the 18 of their papers we have counts for
13 papers · 1 filter
Benchmarking Self-Supervised 3D Medical Vision Foundation Models and Transferability Estimation
Jiaqi Tang, Yu Wang, Yang Liu +7
Self-supervised pretraining has expanded the pool of 3D medical foundation models, but published evaluations use different datasets, endpoints and fine-tuning protocols. Models als…
Engram Neuron Inspired Adaptive Memorization for Lifelong Learning
Suiyang Jiang, Bomin Wang, Hangqi Zhou +2
Lifelong learning enables artificial systems to adapt to changing environments. To achieve this, an effective artificial learner should dynamically reorganize past and new experien…
A generalizable structural brain MRI foundation model via dual-priority federated pretraining
Zhen Yu, Yang Liu, Jianfeng Feng +2
Foundation models hold promise for generalizable analysis of structural brain magnetic resonance imaging (MRI) scans across diverse populations and imaging conditions. However, exi…
3D Consistency Optimization for Self-Supervised Monocular Video Depth Estimation
Yuanye Liu, Ke Zhang, Junzhe Jiang +3
Reliable monocular video depth estimation is crucial for downstream 3D reasoning and embodied AI in endoscopic navigation. However, existing self-supervised approaches typically tr…
Towards 3D heart mesh generation using contactless radar imaging and physics-informed neural network
Jinye Li, Chenxi Fu, Minghang Zheng +3
Cardiac function evaluation necessitates continuous, non-invasive monitoring, a capability limited in MRI. Millimeter-wave (mmWave) radar and its Synthetic Aperture Radar (SAR) mod…
How Far Has AI Come in Liver Fibrosis Staging? A Large-Scale Real-World Dataset and Benchmark
Yuanye Liu, Nannan Shi, Zhejia Zhang +20
Despite years of methodological progress, how far AI has come in liver fibrosis staging has never been systematically evaluated under the heterogeneous, multi-center conditions tha…
X-Edit: Exact, Explicit, and Explainable Null-Space Editing for Medical Vision Transformers
Yuanye Liu, Siyuan Zhou, Ke Zhang +3
Pre-trained Vision Transformers (ViTs) are increasingly deployed for medical image classification. However, correcting their inevitable failure cases in dynamic clinical scenarios…
Liver Fibrosis Quantification and Analysis: The LiQA Dataset and Baseline Method
Yuanye Liu, Hanxiao Zhang, Jiyao Liu +5
Liver fibrosis represents a significant global health burden, necessitating accurate staging for effective clinical management. This report introduces the LiQA (Liver Fibrosis Quan…
Uncertainty-Supervised Interpretable and Robust Evidential Segmentation
Yuzhu Li, An Sui, Fuping Wu +1
Uncertainty estimation has been widely studied in medical image segmentation as a tool to provide reliability, particularly in deep learning approaches. However, previous methods g…
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…
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…
Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis
Yibo Gao, Zheyao Gao, Xin Gao +3
Due to the high stakes in medical decision-making, there is a compelling demand for interpretable deep learning methods in medical image analysis. Concept Bottleneck Models (CBM) h…
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…