6 citations · 12 across the 4 of their papers we have counts for
6 papers
Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling
Chengliang Dai, Shuo Wang, Yuanhan Mo +4
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. As a data-driven…
Efficient Deep Representation Learning by Adaptive Latent Space Sampling
Yuanhan Mo, Shuo Wang, Chengliang Dai +4
Supervised deep learning requires a large amount of training samples with annotations (e.g. label class for classification task, pixel- or voxel-wised label map for segmentation ta…
Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction
Shuo Wang, Chengliang Dai, Yuanhan Mo +3
Gliomas are the most common malignant brain tumourswith intrinsic heterogeneity. Accurate segmentation of gliomas and theirsub-regions on multi-parametric magnetic resonance images…
Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records
Jingqing Zhang, Xiaoyu Zhang, Kai Sun +3
The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications su…
Transfer Learning from Partial Annotations for Whole Brain Segmentation
Chengliang Dai, Yuanhan Mo, Elsa Angelini +2
Brain MR image segmentation is a key task in neuroimaging studies. It is commonly conducted using standard computational tools, such as FSL, SPM, multi-atlas segmentation etc, whic…
Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification
Xiaoyu Zhang, Jingqing Zhang, Kai Sun +3
Different aspects of a clinical sample can be revealed by multiple types of omics data. Integrated analysis of multi-omics data provides a comprehensive view of patients, which has…