726 citations · 859 across the 14 of their papers we have counts for
15 papers
Toward Multimodal Conversational AI for Age-Related Macular Degeneration
Ran Gu, Benjamin Hou, Mélanie Hébert +5
Despite strong performance of deep learning models in retinal disease detection, most systems produce static predictions without clinical reasoning or interactive explanation. Rece…
CT-Bench: A Benchmark for Multimodal Lesion Understanding in Computed Tomography
Qingqing Zhu, Qiao Jin, Tejas S. Mathai +10
Artificial intelligence (AI) can automatically delineate lesions on computed tomography (CT) and generate radiology report content, yet progress is limited by the scarcity of publi…
UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation
Jianghao Wu, Guotai Wang, Ran Gu +6
Domain Adaptation (DA) is important for deep learning-based medical image segmentation models to deal with testing images from a new target domain. As the source-domain data are us…
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…
PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation
Guotai Wang, Xiangde Luo, Ran Gu +6
Background and Objective: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models. Existing toolkits mainly focus on fully…
Learning towards Synchronous Network Memorizability and Generalizability for Continual Segmentation across Multiple Sites
Jingyang Zhang, Peng Xue, Ran Gu +7
In clinical practice, a segmentation network is often required to continually learn on a sequential data stream from multiple sites rather than a consolidated set, due to the stora…
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…
One-shot Weakly-Supervised Segmentation in Medical Images
Wenhui Lei, Qi Su, Ran Gu +5
Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation. One-shot segmentation and weakly-s…
Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation
Ran Gu, Jingyang Zhang, Rui Huang +3
Domain generalizable model is attracting increasing attention in medical image analysis since data is commonly acquired from different institutes with various imaging protocols and…
SS-CADA: A Semi-Supervised Cross-Anatomy Domain Adaptation for Coronary Artery Segmentation
Jingyang Zhang, Ran Gu, Guotai Wang +2
The segmentation of coronary arteries by convolutional neural network is promising yet requires a large amount of labor-intensive manual annotations. Transferring knowledge from re…
Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss
Wenhui Lei, Haochen Mei, Zhengwentai Sun +7
Nasopharyngeal Carcinoma (NPC) is a leading form of Head-and-Neck (HAN) cancer in the Arctic, China, Southeast Asia, and the Middle East/North Africa. Accurate segmentation of Orga…
Automatic Segmentation of Gross Target Volume of Nasopharynx Cancer using Ensemble of Multiscale Deep Neural Networks with Spatial Attention
Haochen Mei, Wenhui Lei, Ran Gu +4
Radiotherapy is the main treatment modality for nasopharynx cancer. Delineation of Gross Target Volume (GTV) from medical images such as CT and MRI images is a prerequisite for rad…
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…
CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation
Ran Gu, Guotai Wang, Tao Song +6
Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance f…