869 citations · 2.4k across the 52 of their papers we have counts for
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DINOv3-Guided Cross Fusion Framework for Semantic-aware CT generation from MRI and CBCT
Xianhao Zhou, Jianghao Wu, Ku Zhao +5
Generating synthetic CT images from CBCT or MRI has a potential for efficient radiation dose planning and adaptive radiotherapy. However, existing CNN-based models lack global sema…
MetaSSL: A General Heterogeneous Loss for Semi-Supervised Medical Image Segmentation
Weiren Zhao, Lanfeng Zhong, Xin Liao +4
Semi-Supervised Learning (SSL) is important for reducing the annotation cost for medical image segmentation models. State-of-the-art SSL methods such as Mean Teacher, FixMatch and…
MedCAL-Bench: A Comprehensive Benchmark on Cold-Start Active Learning with Foundation Models for Medical Image Analysis
Ning Zhu, Xiaochuan Ma, Shaoting Zhang +1
Cold-Start Active Learning (CSAL) aims to select informative samples for annotation without prior knowledge, which is important for improving annotation efficiency and model perfor…
OpenPath: Open-Set Active Learning for Pathology Image Classification via Pre-trained Vision-Language Models
Lanfeng Zhong, Xin Liao, Shichuan Zhang +2
Pathology image classification plays a crucial role in accurate medical diagnosis and treatment planning. Training high-performance models for this task typically requires large-sc…
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp +67
Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI an…
Dynamic Gradient Sparsification Training for Few-Shot Fine-tuning of CT Lymph Node Segmentation Foundation Model
Zihao Luo, Zijun Gao, Wenjun Liao +3
Accurate lymph node (LN) segmentation is critical in radiotherapy treatment and prognosis analysis, but is limited by the need for large annotated datasets. While deep learning-bas…
Fairness Analysis of CLIP-Based Foundation Models for X-Ray Image Classification
Xiangyu Sun, Xiaoguang Zou, Yuanquan Wu +2
X-ray imaging is pivotal in medical diagnostics, offering non-invasive insights into a range of health conditions. Recently, vision-language models, such as the Contrastive Languag…
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentation
Xiaochuan Ma, Jia Fu, Wenjun Liao +2
Brain tumor segmentation is important for diagnosis of the tumor, and current deep-learning methods rely on a large set of annotated images for training, with high annotation costs…
GLFC: Unified Global-Local Feature and Contrast Learning with Mamba-Enhanced UNet for Synthetic CT Generation from CBCT
Xianhao Zhou, Jianghao Wu, Huangxuan Zhao +3
Generating synthetic Computed Tomography (CT) images from Cone Beam Computed Tomography (CBCT) is desirable for improving the image quality of CBCT. Existing synthetic CT (sCT) gen…