4 citations · 8 across the 18 of their papers we have counts for
20 papers
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…
HEDL: Hyper Evidential Deep Learning for Hierarchical Classification
Yuanye Liu, Xiahai Zhuang
Fine-grained recognition often involves hierarchical label spaces, where a model may be confident about a coarse semantic concept while remaining uncertain among its descendant cla…
ReCBM: Uncertainty-Gated Relational Reasoning for Concept Bottleneck Models
An Sui, Yuzhu Li, Fuping Wu +1
Concept Bottleneck Models (CBMs) provide an interpretable framework by grounding predictions in human-understandable concepts, enabling semantic inspection and test-time interventi…
A Self-Evolving Agent for Longitudinal Personal Health Management
Haoran Li, Jiebi Deng, Tong Jin +11
Medical large language models and agents show strong single-encounter question answering and evidence interpretation but struggle to accumulate and govern experience across encount…
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…
Generalized Evidential Deep Learning: From a Bayesian Perspective
Yuanye Liu, Yibo Gao, Yuanyang Chen +1
Evidential Deep Learning (EDL) has emerged as an efficient, sampling-free strategy for uncertainty estimation. A series of EDL variants have been proposed to address specific limit…
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…
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…
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…
Selection, Ensemble, and Adaptation: Advancing Multi-Source-Free Domain Adaptation via Architecture Zoo
Jiangbo Pei, Ruizhe Li, Aidong Men +3
Conventional Multi-Source Free Domain Adaptation (MSFDA) assumes that each source domain provides a single source model, and all source models adopt a uniform architecture. This pa…
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…