14 citations · 45 across the 28 of their papers we have counts for
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Cross-View Consistency Regularisation for Knowledge Distillation
Weijia Zhang, Dongnan Liu, Weidong Cai +1
Knowledge distillation (KD) is an established paradigm for transferring privileged knowledge from a cumbersome model to a lightweight and efficient one. In recent years, logit-base…
ORID: Organ-Regional Information Driven Framework for Radiology Report Generation
Tiancheng Gu, Kaicheng Yang, Xiang An +3
The objective of Radiology Report Generation (RRG) is to automatically generate coherent textual analyses of diseases based on radiological images, thereby alleviating the workload…
AMNCutter: Affinity-Attention-Guided Multi-View Normalized Cutter for Unsupervised Surgical Instrument Segmentation
Mingyu Sheng, Jianan Fan, Dongnan Liu +2
Surgical instrument segmentation (SIS) is pivotal for robotic-assisted minimally invasive surgery, assisting surgeons by identifying surgical instruments in endoscopic video frames…
DeepIcon: A Hierarchical Network for Layer-wise Icon Vectorization
Qi Bing, Chaoyi Zhang, Weidong Cai
In contrast to the well-established technique of rasterization, vectorization of images poses a significant challenge in the field of computer graphics. Recent learning-based metho…
TractShapeNet: Efficient Multi-Shape Learning with 3D Tractography Point Clouds
Yui Lo, Yuqian Chen, Dongnan Liu +9
Brain imaging studies have demonstrated that diffusion MRI tractography geometric shape descriptors can inform the study of the brain's white matter pathways and their relationship…
The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning study
Yui Lo, Yuqian Chen, Dongnan Liu +9
The shape of the brain's white matter connections is relatively unexplored in diffusion MRI tractography analysis. While it is known that tract shape varies in populations and acro…
Enhancing Advanced Visual Reasoning Ability of Large Language Models
Zhiyuan Li, Dongnan Liu, Chaoyi Zhang +3
Recent advancements in Vision-Language (VL) research have sparked new benchmarks for complex visual reasoning, challenging models' advanced reasoning ability. Traditional Vision-La…
Revisiting Surgical Instrument Segmentation Without Human Intervention: A Graph Partitioning View
Mingyu Sheng, Jianan Fan, Dongnan Liu +2
Surgical instrument segmentation (SIS) on endoscopic images stands as a long-standing and essential task in the context of computer-assisted interventions for boosting minimally in…
CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discrimination
Kaicheng Yang, Tiancheng Gu, Xiang An +5
Contrastive Language-Image Pre-training (CLIP) has achieved excellent performance over a wide range of tasks. However, the effectiveness of CLIP heavily relies on a substantial cor…
Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View
Jianan Fan, Dongnan Liu, Canran Li +5
Cellular nuclei recognition serves as a fundamental and essential step in the workflow of digital pathology. However, with disparate source organs and staining procedures among his…
White Matter Geometry-Guided Score-Based Diffusion Model for Tissue Microstructure Imputation in Tractography Imaging
Yui Lo, Yuqian Chen, Fan Zhang +6
Parcellation of white matter tractography provides anatomical features for disease prediction, anatomical tract segmentation, surgical brain mapping, and non-imaging phenotype clas…
TractGraphFormer: Anatomically Informed Hybrid Graph CNN-Transformer Network for Interpretable Sex and Age Prediction from Diffusion MRI Tractography
Yuqian Chen, Fan Zhang, Meng Wang +10
The relationship between brain connections and non-imaging phenotypes is increasingly studied using deep neural networks. However, the local and global properties of brain white ma…
RWKV-CLIP: A Robust Vision-Language Representation Learner
Tiancheng Gu, Kaicheng Yang, Xiang An +4
Contrastive Language-Image Pre-training (CLIP) has significantly improved performance in various vision-language tasks by expanding the dataset with image-text pairs obtained from…
Boosting 3D Neuron Segmentation with 2D Vision Transformer Pre-trained on Natural Images
Yik San Cheng, Runkai Zhao, Heng Wang +2
Neuron reconstruction, one of the fundamental tasks in neuroscience, rebuilds neuronal morphology from 3D light microscope imaging data. It plays a critical role in analyzing the s…
Dance Any Beat: Blending Beats with Visuals in Dance Video Generation
Xuanchen Wang, Heng Wang, Dongnan Liu +1
Generating dance from music is crucial for advancing automated choreography. Current methods typically produce skeleton keypoint sequences instead of dance videos and lack the capa…
LaPA: Latent Prompt Assist Model For Medical Visual Question Answering
Tiancheng Gu, Kaicheng Yang, Dongnan Liu +1
Medical visual question answering (Med-VQA) aims to automate the prediction of correct answers for medical images and questions, thereby assisting physicians in reducing repetitive…
Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling
Jianan Fan, Dongnan Liu, Hang Chang +3
Machine learning holds tremendous promise for transforming the fundamental practice of scientific discovery by virtue of its data-driven nature. With the ever-increasing stream of…
Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction
Yui Lo, Yuqian Chen, Dongnan Liu +8
Shape plays an important role in computer graphics, offering informative features to convey an object's morphology and functionality. Shape analysis in brain imaging can help inter…
Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive Distillation
Yaxuan Song, Jianan Fan, Dongnan Liu +1
Source-free domain adaptation (SFDA) alleviates the domain discrepancy among data obtained from domains without accessing the data for the awareness of data privacy. However, exist…
Learning to Generalize over Subpartitions for Heterogeneity-aware Domain Adaptive Nuclei Segmentation
Jianan Fan, Dongnan Liu, Hang Chang +1
Annotation scarcity and cross-modality/stain data distribution shifts are two major obstacles hindering the application of deep learning models for nuclei analysis, which holds a b…