Multi-center Medical Data Mining with FL-Net - A One-stop Shop for Federated Learning
本文针对多中心医疗数据挖掘中联邦学习框架不满足实际需求的问题，提出了一种新的解决方案FL-Net，它集成了多种功能以支持隐私保护下的协作研究。
0 citationsRead paper
Selected work
Representative Papers
Multi-Teacher Distillation for Cross-Domain Streaming Electrolaryngeal Speech Encoding
为解决电喉语音编码问题，提出多教师知识蒸馏框架训练轻量级流式内容编码器，通过自监督学习模型和微调识别模型提供目标，降低电喉语音错误率。
0 citationsRead paper
PyDoseRT Proton: A GPU Pencil-Beam Engine with a Convolutional Residual-Correction Network for Fast Proton Dose Calculation
本文使用结合物理基础的铅笔束剂量引擎与3D卷积残差校正网络的方法，解决了快速准确计算质子剂量的问题。
0 citationsRead paper
Less Contouring, More Accuracy: Lesion-Guided ROI Deep Learning for Ovarian Ultrasound Classification
研究通过病变引导的ROI深度学习方法，提高了卵巢超声分类准确性并减少了标注负担，使用多种模型比较得出MaxViT-Tiny在两个数据集上表现最佳。
0 citationsRead paper
Synthesizing Post-Acetazolamide Cerebral Blood Flow Maps from Baseline MRI in Moyamoya Using 3D Generative AI
This study addresses the challenge of assessing cerebrovascular reserve in Moyamoya disease patients with acetazolamide contraindications by proposing CAE3D, a three-dimensional conditional autoencoder that non-invasively synthesizes post-challenge cerebral blood flow maps from baseline ASL images. This work represents the first validation of retrospectively generating stress perfusion data from baseline MRI using a deterministic architecture integrating perfusion imaging with deep learning. Experimental results demonstrate superior performance over various 3D generative and foundation models, achieving a mean absolute error of 0.066, a structural similarity index of 0.80, and near-zero whole-brain bias. Consequently, this method establishes a novel paradigm for precise hemodynamic assessment in contraindicated patients, effectively bridging advanced deep learning techniques with clinical neuroimaging requirements to overcome diagnostic limitations associated with pharmacological vasodilators.
0 citationsRead paper
Recent publications
Latest Papers
Multi-center Medical Data Mining with FL-Net - A One-stop Shop for Federated Learning
本文针对多中心医疗数据挖掘中联邦学习框架不满足实际需求的问题，提出了一种新的解决方案FL-Net，它集成了多种功能以支持隐私保护下的协作研究。
0 citationsRead paper
Multi-Teacher Distillation for Cross-Domain Streaming Electrolaryngeal Speech Encoding
为解决电喉语音编码问题，提出多教师知识蒸馏框架训练轻量级流式内容编码器，通过自监督学习模型和微调识别模型提供目标，降低电喉语音错误率。
0 citationsRead paper
PyDoseRT Proton: A GPU Pencil-Beam Engine with a Convolutional Residual-Correction Network for Fast Proton Dose Calculation
本文使用结合物理基础的铅笔束剂量引擎与3D卷积残差校正网络的方法，解决了快速准确计算质子剂量的问题。
0 citationsRead paper
Less Contouring, More Accuracy: Lesion-Guided ROI Deep Learning for Ovarian Ultrasound Classification
研究通过病变引导的ROI深度学习方法，提高了卵巢超声分类准确性并减少了标注负担，使用多种模型比较得出MaxViT-Tiny在两个数据集上表现最佳。
0 citationsRead paper
Synthesizing Post-Acetazolamide Cerebral Blood Flow Maps from Baseline MRI in Moyamoya Using 3D Generative AI
This study addresses the challenge of assessing cerebrovascular reserve in Moyamoya disease patients with acetazolamide contraindications by proposing CAE3D, a three-dimensional conditional autoencoder that non-invasively synthesizes post-challenge cerebral blood flow maps from baseline ASL images. This work represents the first validation of retrospectively generating stress perfusion data from baseline MRI using a deterministic architecture integrating perfusion imaging with deep learning. Experimental results demonstrate superior performance over various 3D generative and foundation models, achieving a mean absolute error of 0.066, a structural similarity index of 0.80, and near-zero whole-brain bias. Consequently, this method establishes a novel paradigm for precise hemodynamic assessment in contraindicated patients, effectively bridging advanced deep learning techniques with clinical neuroimaging requirements to overcome diagnostic limitations associated with pharmacological vasodilators.
0 citationsRead paper