Mohsin Bilal,Robert Jewsbury,Ruoyu Wang et al.
Mohsin Bilal et al.
Image analysis and machine learning algorithms operating on multi-gigapixel whole-slide images (WSIs) often process a large number of tiles (sub-images) and require aggregating predictions from the tiles in order to predict WSI-level labels...
A novel one-to-multiple unsupervised domain adaptation framework for abdominal organ segmentation [0.03%]
一种新颖的一对多无监督领域适应框架用于腹部器官分割
Xiaowei Xu,Yinan Chen,Jianghao Wu et al.
Xiaowei Xu et al.
Abdominal multi-organ segmentation in multi-sequence magnetic resonance images (MRI) is of great significance in many clinical scenarios, e.g., MRI-oriented pre-operative treatment planning. Labeling multiple organs on a single MR sequence ...
Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation [0.03%]
基于模糊选择一致性的半监督医学图像分割均值教师方法
Zhe Xu,Yixin Wang,Donghuan Lu et al.
Zhe Xu et al.
Semi-supervised learning has greatly advanced medical image segmentation since it effectively alleviates the need of acquiring abundant annotations from experts, wherein the mean-teacher model, known as a milestone of perturbed consistency ...
Zhuo-Xu Cui,Sen Jia,Chentao Cao et al.
Zhuo-Xu Cui et al.
Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data. However, the existing UNN-based approaches lack...
Cross-dimensional transfer learning in medical image segmentation with deep learning [0.03%]
基于深度学习的跨维度医学图像分割迁移学习
Hicham Messaoudi,Ahror Belaid,Douraied Ben Salem et al.
Hicham Messaoudi et al.
Over the last decade, convolutional neural networks have emerged and advanced the state-of-the-art in various image analysis and computer vision applications. The performance of 2D image classification networks is constantly improving and b...
Alper Güngör,Salman Uh Dar,Şaban Öztürk et al.
Alper Güngör et al.
Deep MRI reconstruction is commonly performed with conditional models that de-alias undersampled acquisitions to recover images consistent with fully-sampled data. Since conditional models are trained with knowledge of the imaging operator,...
Lei Li,Wangbin Ding,Liqin Huang et al.
Lei Li et al.
Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and imp...
Tong Yu,Pietro Mascagni,Juan Verde et al.
Tong Yu et al.
Searching through large volumes of medical data to retrieve relevant information is a challenging yet crucial task for clinical care. However the primitive and most common approach to retrieval, involving text in the form of keywords, is se...
Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available [0.03%]
基于最优传输的跨图谱映射(CAROT):在没有原始数据的情况下创建不同图谱的连接组
Javid Dadashkarimi,Amin Karbasi,Qinghao Liang et al.
Javid Dadashkarimi et al.
Open-source, publicly available neuroimaging datasets - whether from large-scale data collection efforts or pooled from multiple smaller studies - offer unprecedented sample sizes and promote generalization efforts. Releasing data can democ...
SMILE: Cost-sensitive multi-task learning for nuclear segmentation and classification with imbalanced annotations [0.03%]
基于成本敏感的多任务学习方法联合标记不平衡条件下的核分裂分割与分类
Xipeng Pan,Jijun Cheng,Feihu Hou et al.
Xipeng Pan et al.
High throughput nuclear segmentation and classification of whole slide images (WSIs) is crucial to biological analysis, clinical diagnosis and precision medicine. With the advances of CNN algorithms and the continuously growing datasets, co...