4 citations · 12 across the 22 of their papers we have counts for
12 papers · 1 filter
UniPET: a universal network for high-quality PET image denoising across varied dose reduction factors
Zhiwen Yang, Yang Zhou, Haowei Chen +4
Most existing deep learning-based PET image denoising methods assume a fixed and known dose reduction factor (DRF) for low-dose PET images. However, these methods encounter signifi…
A co-evolving agentic AI system for medical imaging analysis
Songhao Li, Jonathan Xu, Tiancheng Bao +11
Agentic AI is rapidly advancing in healthcare and biomedical research. However, in medical image analysis, their performance and adoption remain limited due to the lack of a robust…
MFM-DA: Instance-Aware Adaptor and Hierarchical Alignment for Efficient Domain Adaptation in Medical Foundation Models
Jia-Xuan Jiang, Wenhui Lei, Yifeng Wu +5
Medical Foundation Models (MFMs), trained on large-scale datasets, have demonstrated superior performance across various tasks. However, these models still struggle with domain gap…
Unifying Multiple Foundation Models for Advanced Computational Pathology
Wenhui Lei, Yusheng Tan, Anqi Li +7
Foundation models have substantially advanced computational pathology by learning transferable visual representations from large histological datasets, yet their performance varies…
Unleashing the Potential of Vision-Language Pre-Training for 3D Zero-Shot Lesion Segmentation via Mask-Attribute Alignment
Yankai Jiang, Wenhui Lei, Xiaofan Zhang +1
Recent advancements in medical vision-language pre-training models have driven significant progress in zero-shot disease recognition. However, transferring image-level knowledge to…
Labeled-to-Unlabeled Distribution Alignment for Partially-Supervised Multi-Organ Medical Image Segmentation
Xixi Jiang, Dong Zhang, Xiang Li +3
Partially-supervised multi-organ medical image segmentation aims to develop a unified semantic segmentation model by utilizing multiple partially-labeled datasets, with each datase…
MedIAnomaly: A comparative study of anomaly detection in medical images
Yu Cai, Weiwen Zhang, Hao Chen +1
Anomaly detection (AD) aims at detecting abnormal samples that deviate from the expected normal patterns. Generally, it can be trained merely on normal data, without a requirement…
Efficient Subclass Segmentation in Medical Images
Linrui Dai, Wenhui Lei, Xiaofan Zhang
As research interests in medical image analysis become increasingly fine-grained, the cost for extensive annotation also rises. One feasible way to reduce the cost is to annotate w…
CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation
Ran Gu, Guotai Wang, Jiangshan Lu +8
Generalization to previously unseen images with potential domain shifts and different styles is essential for clinically applicable medical image segmentation, and the ability to d…
Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures
Ran Gu, Jingyang Zhang, Guotai Wang +5
Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for medical image segmentation, yet need plenty of manual annotations for training. Semi-Supervised…
Contrastive Domain Disentanglement for Generalizable Medical Image Segmentation
Ran Gu, Jiangshan Lu, Jingyang Zhang +4
Efficiently utilizing discriminative features is crucial for convolutional neural networks to achieve remarkable performance in medical image segmentation and is also important for…
Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images
Wenhui Lei, Wei Xu, Ran Gu +3
Deep learning networks have shown promising performance for accurate object localization in medial images, but require large amount of annotated data for supervised training, which…