A hybrid deep learning framework for real-time speckle reduction and image enhancement on portable ultrasound systems [0.03%]
便携式超声系统实时斑点减少和图像增强的混合深度学习框架
Hyunwoo Cho,Jaeseok Lee,Jongsoo Lee et al.
Hyunwoo Cho et al.
Background: Speckle patterns in ultrasound images often obscure anatomical details, leading to diagnostic uncertainty. Recently, various deep learning-based techniques have been introduced to effectively suppress speckle;...
Evaluation of AI-derived LAD artery dose metrics for survival stratification in stage III NSCLC: A secondary analysis of RTOG 0617 trial [0.03%]
基于人工智能的LAD动脉剂量参数在III期NSCLC生存预后中的评估：RTOG 0617试验的二次分析
Libing Zhu,Yi Rong,Randa Tao et al.
Libing Zhu et al.
Background: While the radiation dose to left anterior descending (LAD) artery is associated with overall survival (OS) in NSCLC patients, manual segmentation is labor-intensive. Several auto-segmentation models were devel...
Multicenter Study
Medical physics. 2026 Aug;53(8):e70621. DOI:10.1002/mp.70621 2026
Real-world assessment of a deep learning neural network algorithm for prostate cancer detection in MRI using true de novo data [0.03%]
使用真实新颖数据评估MRI中基于深度学习神经网络的前列腺癌检测算法
Steven M Shea,Michael Wesolowski,Abdulrahman Hashem et al.
Steven M Shea et al.
Background: Deep-learning neural network algorithms for detecting prostate cancer in MRI have proliferated in the literature. However, out of 30+ studies published since the PROSTATEx challenge, no studies tested the perf...
Ultrasound-based thyroid nodule segmentation with deep hybrid convolutional network [0.03%]
基于超声的甲状腺结节分割的深度混合卷积网络方法
Fan Lu,Hao Sun,Binbin Jiang et al.
Fan Lu et al.
Background: Automatic segmentation of ultrasound-based thyroid nodules can assist physicians in more efficiently and accurately assessing thyroid diseases. However, thyroid ultrasound imaging presents certain unique chall...
Photon minibeam-based LATTICE radiotherapy for small and medium-sized tumors: A dosimetric planning study [0.03%]
基于光子微型束的LATTICE放射治疗的小型和中型肿瘤剂量学计划研究
Wei Wu,Nimita Shinde,Jiaxin Li et al.
Wei Wu et al.
Background: Lattice radiotherapy (LRT) is a spatially fractionated technique that delivers three-dimensional high-dose vertices within tumors. However, conventional LRT is constrained by geometric limitations when applied...
Improving dual-panel in-beam PET imaging for proton therapy monitoring using 3D U-Net [0.03%]
使用3D U-Net改进双面板中束PET成像以监测质子治疗
Dengyun Mu,Pengyuan Qi,Ao Qiu et al.
Dengyun Mu et al.
Background: In-beam positron emission tomography (PET) integrates dedicated detectors into proton therapy systems, enabling real-time acquisition of proton-induced positron-emitting activity. By pre-delivering a subset of...
Translational tumor control probability modeling for NSCLC: A two-dimensional maximum-likelihood framework [0.03%]
基于最大概似估计的非小细胞肺癌转移灶控制概率模型研究新方法及临床应用探索
Ryoichi Hinoto,Takeji Sakae,Kenta Takada et al.
Ryoichi Hinoto et al.
Background: Tumor control probability (TCP) modeling for early-stage non-small cell lung cancer (NSCLC) is usually performed by one-dimensional (1D) fitting that assumes error-free dose and assigns all uncertainty to the ...
Reference-Free large language model agents for physician-guided radiotherapy treatment planning [0.03%]
参考无关的大语言模型代理在医师指导的放射治疗计划中的应用
Dongrong Yang,Xin Wu,Yibo Xie et al.
Dongrong Yang et al.
Background: Large language models (LLMs) have recently demonstrated exceptional capabilities, offering the potential to streamline workflows and enhance efficiency across diverse tasks. However, their application in domai...
Knowledge-distilled diffusion models for improving cone-beam CT image quality with meta-learning under imbalanced data [0.03%]
基于元学习的不平衡数据下改进CBCT图像质量的知识蒸馏扩散模型
Joonil Hwang,Sangjoon Park,Seungryong Cho et al.
Joonil Hwang et al.
Background: Adaptive radiation therapy (ART) relies on daily cone-beam CT (CBCT), yet its limited image quality hinders accurate dose calculation, particularly under substantial anatomical changes. ...
An improved nnUNet with Axial Attention Mechanism and Attention Gated Units for Efficient and Accurate Kidney Segmentation in CT Images [0.03%]
一种改进的Axial注意力机制和注意门控单元的nnUNet算法在CT图像中进行高效且准确的肾脏分割的方法
Malong Tan,Renchao Jin,Xiangyang Xu et al.
Malong Tan et al.
Background: Accurate segmentation of computed tomography (CT) images plays a vital role in surgical planning and therapeutic evaluation for kidney disease diagnosis and treatment. Although nnUNet is widely used in medical...