BrachyPlan: A fine-grained efficient dose-guided inverse planning strategy for low-dose-rate brachytherapy [0.03%]
BrachyPlan: 一种用于低剂量率组织间插植治疗的细粒度、高效且基于剂量引导的逆向计划策略
Jiaxuan Liu,Haitao Li,Haochen Shi et al.
Jiaxuan Liu et al.
Brachytherapy delivers highly conformal doses for malignancies ranging from pancreatic to head-and-neck cancers, yet today's treatment-planning systems still depend on extensive manual manipulation and dose engines of uncertain accuracy. We...
3D masked autoencoder with spatiotemporal transformer for modeling of 4D fMRI data [0.03%]
具有时空变换器的3D掩码自动编码器用于4D fMRI数据分析建模
Jie Gao,Bao Ge,Ning Qiang et al.
Jie Gao et al.
Functional magnetic resonance imaging (fMRI) is a crucial tool in neuroscience for capturing dynamic brain activity across spatial and temporal dimensions. However, fMRI data are high-dimensional, spatiotemporal interdependent, and often no...
Unpaired volumetric harmonization of brain MRI with conditional latent diffusion [0.03%]
基于条件潜在扩散的未配对脑MRI体积谐调
Mengqi Wu,Minhui Yu,Shuaiming Jing et al.
Mengqi Wu et al.
Multi-site structural MRI is increasingly used in neuroimaging studies to diversify subject cohorts. However, combining MR images acquired from various sites/centers may introduce site-related non-biological variations. Retrospective image ...
Two-level semi-supervised collaborative medical image segmentation with bidirectional knowledge exchange [0.03%]
双向知识交流的两层半监督协作医学图像分割方法
Zhongda Zhao,Haiyan Wang,Tao Lei et al.
Zhongda Zhao et al.
Traditional co-training methods fail to leverage ensemble learning effectively, resulting in resource waste. To address this, we propose a two-level co-training structure. The first-level models follow a classical co-training approach, whil...
BUFNet: Boundary-aware and uncertainty-driven multi-modal fusion network for MR brain tumor segmentation [0.03%]
基于边界感知和不确定性驱动的多模态融合网络用于磁共振脑肿瘤图像分割
Tongxue Zhou,Su Ruan,Baiying Lei
Tongxue Zhou
Brain tumor segmentation plays a critical role in the diagnosis and treatment planning of brain tumors. However, achieving accurate segmentation is challenging due to the complex boundaries between different tumor sub-regions. Additionally,...
AdverIN: Monotonic adversarial intensity attack for domain generalization in medical image segmentation [0.03%]
用于医学图像分割的领域泛化单调对抗强度攻击
Zheyuan Zhang,Bin Wang,Lanhong Yao et al.
Zheyuan Zhang et al.
Domain generalization (DG) has emerged as a promising research direction because it can potentially enable deep learning models to handle data from previously unseen domains. DG methods try to achieve this by learning domain-invariant featu...
CIMB-MVQA: Causal intervention on modality-specific biases for medical visual question answering [0.03%]
基于模态特异偏差的因果干预的医学视觉问答研究
Bing Liu,Lijun Liu,Jiaman Ding et al.
Bing Liu et al.
Medical Visual Question Answering (Med-VQA) systems frequently rely on spurious visual and language cues produced by dataset biases and structural con-founders, which undermines robustness and real-world generalization. To alleviate spuriou...
Compact programmable transmit scheme for contrast imaging using nonlinear difference-frequency ultrasound signals [0.03%]
一种用于非线性差频超声信号对比度成像的紧凑可编程发射方案
Dong Hun Kim,Dong-Hyun Kang,Jun Hong Park et al.
Dong Hun Kim et al.
The nonlinear interaction between two acoustic waves at different primary frequencies generates a signal with a frequency equal to the difference between these primary frequencies. This signal is proportional to the nonlinear elasticity of ...
ARDMR: Adaptive recursive inference and representation disentanglement for multimodal large deformation registration [0.03%]
自适应递归推理和多模态大变形配准表示分离方法
Yibo Hu,Qi Zhang,Ziqi Zhao et al.
Yibo Hu et al.
Deformable registration of multimodal medical images constitutes a fundamental task in many medical image analysis applications, particularly in the diagnosis and treatment of liver cancer where different modality images are frequently empl...
M3Surv: Fusing Multi-slide and Multi-omics for Memory-augmented robust Survival prediction [0.03%]
M3Surv：融合多张切片和多组学数据以增强记忆的生存预测方法
Mingcheng Qu,Guang Yang,Donglin Di et al.
Mingcheng Qu et al.
Multimodal survival prediction is crucial for personalized oncology. However, existing methods typically integrate only Formalin-Fixed Paraffin-Embedded (FFPE) slides with a single omics type, such as genomics, overlooking Fresh Frozen (FF)...