[HTML][HTML] 深度学习在医学影像中的应用综述
施俊， 汪琳琳， 王珊珊， 陈艳霞， 王乾， 魏冬铭， 梁淑君… - 2020 - cjig.cn
摘要深度学习能自动从大样本数据中学习获得优良的特征表达, 有效提升各种机器学习任务的
性能, 已广泛应用于信号处理, 计算机视觉和自然语言处理等诸多领域. 基于深度学习的医学影像 …
性能, 已广泛应用于信号处理, 计算机视觉和自然语言处理等诸多领域. 基于深度学习的医学影像 …
Predicting infarct core from computed tomography perfusion in acute ischemia with machine learning: Lessons from the ISLES challenge
Background and Purpose: The ISLES challenge (Ischemic Stroke Lesion Segmentation)
enables globally diverse teams to compete to develop advanced tools for stroke lesion …
enables globally diverse teams to compete to develop advanced tools for stroke lesion …
Adaptive feature recombination and recalibration for semantic segmentation with fully convolutional networks
Fully convolutional networks have been achieving remarkable results in image semantic
segmentation, while being efficient. Such efficiency results from the capability of segmenting …
segmentation, while being efficient. Such efficiency results from the capability of segmenting …
Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning
CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke.
Conventional perfusion analysis performs a deconvolution of the measurements and …
Conventional perfusion analysis performs a deconvolution of the measurements and …
Predicting treatment-specific lesion outcomes in acute ischemic stroke from 4D CT perfusion imaging using spatio-temporal convolutional neural networks
For the diagnosis and precise treatment of acute ischemic stroke, predicting the final location
and volume of lesions is of great clinical interest. Current deep learning-based prediction …
and volume of lesions is of great clinical interest. Current deep learning-based prediction …
Multi-modal tumor segmentation with deformable aggregation and uncertain region inpainting
Multi-modal tumor segmentation exploits complementary information from different
modalities to help recognize tumor regions. Known multi-modal segmentation methods …
modalities to help recognize tumor regions. Known multi-modal segmentation methods …
Learning cross-modal deep representations for multi-modal MR image segmentation
Multi-modal magnetic resonance imaging (MRI) is essential in clinics for comprehensive
diagnosis and surgical planning. Nevertheless, the segmentation of multi-modal MR images …
diagnosis and surgical planning. Nevertheless, the segmentation of multi-modal MR images …
Brain SegNet: 3D local refinement network for brain lesion segmentation
MR images (MRIs) accurate segmentation of brain lesions is important for improving cancer
diagnosis, surgical planning, and prediction of outcome. However, manual and accurate …
diagnosis, surgical planning, and prediction of outcome. However, manual and accurate …
[HTML][HTML] FeMA: Feature matching auto-encoder for predicting ischaemic stroke evolution and treatment outcome
Although, predicting ischaemic stroke evolution and treatment outcome provide important
information one step towards individual treatment planning, classifying functional outcome …
information one step towards individual treatment planning, classifying functional outcome …
Combining unsupervised and supervised learning for predicting the final stroke lesion
Predicting the final ischaemic stroke lesion provides crucial information regarding the
volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision …
volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision …