67 citations · 140 across the 48 of their papers we have counts for
7 papers · 1 filter
SurgiATM: A Physics-Guided Plug-and-Play Model for Deep Learning-Based Smoke Removal in Laparoscopic Surgery
Mingyu Sheng, Jianan Fan, Dongnan Liu +3
During laparoscopic surgery, smoke generated by tissue cauterization can significantly degrade the visual quality of endoscopic frames, increasing the risk of surgical errors and h…
Towards Conditional Feature Alignment for Cross-Domain Counting
Zhuonan Liang, Dongnan Liu, Jianan Fan +6
Object counting models often degrade under cross-domain deployment because density composition varies across domains and is itself task-relevant. Standard feature alignment methods…
A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography
Yui Lo, Yuqian Chen, Dongnan Liu +8
Shape measures have emerged as promising descriptors of white matter tractography, offering complementary insights into anatomical variability and associations with cognitive and c…
MultiCo3D: Multi-Label Voxel Contrast for One-Shot Incremental Segmentation of 3D Neuroimages
Hao Xu, Tengfei Xue, Dongnan Liu +6
3D neuroimages provide a comprehensive view of brain structure and function, aiding in precise localization and functional connectivity analysis. Segmentation of white matter (WM)…
MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention
Tianyi Wang, Jianan Fan, Dingxin Zhang +4
Histopathology and transcriptomics are fundamental modalities in oncology, encapsulating the morphological and molecular aspects of the disease. Multi-modal self-supervised learnin…
Medical Image Registration Meets Vision Foundation Model: Prototype Learning and Contour Awareness
Hao Xu, Tengfei Xue, Jianan Fan +7
Medical image registration is a fundamental task in medical image analysis, aiming to establish spatial correspondences between paired images. However, existing unsupervised deform…
RealSyn: An Effective and Scalable Multimodal Interleaved Document Transformation Paradigm
Tiancheng Gu, Kaicheng Yang, Chaoyi Zhang +6
After pre-training on extensive image-text pairs, Contrastive Language-Image Pre-training (CLIP) demonstrates promising performance on a wide variety of benchmarks. However, a subs…