Models, code, and papers for "Weiliang Xu":
Loop Closure Detection (LCD) is the essential module in the simultaneous localization and mapping (SLAM) task. In the current appearance-based SLAM methods, the visual inputs are usually affected by illumination, appearance and viewpoints changes. Comparing to the visual inputs, with the active property, light detection and ranging (LiDAR) based point-cloud inputs are invariant to the illumination and appearance changes. In this paper, we extract 3D voxel maps and 2D top view maps from LiDAR inputs, and the former could capture the local geometry into a simplified 3D voxel format, the later could capture the local road structure into a 2D image format. However, the most challenge problem is to obtain efficient features from 3D and 2D maps to against the viewpoints difference. In this paper, we proposed a synchronous adversarial feature learning method for the LCD task, which could learn the higher level abstract features from different domains without any label data. To the best of our knowledge, this work is the first to extract multi-domain adversarial features for the LCD task in real time. To investigate the performance, we test the proposed method on the KITTI odometry dataset. The extensive experiments results show that, the proposed method could largely improve LCD accuracy even under huge viewpoints differences.
Genome-wide association studies (GWASs) aim to detect genetic risk factors for complex human diseases by identifying disease-associated single-nucleotide polymorphisms (SNPs). SNP-wise approach, the standard method for analyzing GWAS, tests each SNP individually. Then the P-values are adjusted for multiple testing. Multiple testing adjustment (purely based on p-values) is over-conservative and causes lack of power in many GWASs, due to insufficiently modelling the relationship among SNPs. To address this problem, we propose a novel method, which borrows information across SNPs by grouping SNPs into three clusters. We pre-specify the patterns of clusters by minor allele frequencies of SNPs between cases and controls, and enforce the patterns with prior distributions. Therefore, compared with the traditional approach, it better controls false discovery rate (FDR) and shows higher sensitivity, which is confirmed by our simulation studies. We re-analyzed real data studies on identifying SNPs associated with severe bortezomib-induced peripheral neuropathy (BiPN) in patients with multiple myeloma. The original analysis in the literature failed to identify SNPs after FDR adjustment. Our proposed method not only detected the reported SNPs after FDR adjustment but also discovered a novel SNP rs4351714 that has been reported to be related to multiple myeloma in another study.
Cardiovascular Magnetic Resonance (CMR) plays an important role in the diagnoses and treatment of cardiovascular diseases while motion artifacts which are formed during the scanning process of CMR seriously affects doctors to find the exact focus. The current correction methods mainly focus on the K-space which is a grid of raw data obtained from the MR signal directly and then transfer to CMR image by inverse Fourier transform. They are neither effective nor efficient and can not be utilized in clinic. In this paper, we propose a novel approach for CMR motion artifact correction using deep learning. Specially, we use deep residual network (ResNet) as net framework and train our model in adversarial manner. Our approach is motivated by the connection between image motion blur and CMR motion artifact, so we can transfer methods from motion-deblur where deep learning has made great progress to CMR motion-correction successfully. To evaluate motion artifact correction methods, we propose a novel algorithm on how edge detection results are improved by deblurred algorithm. Boosted by deep learning and adversarial training algorithm, our model is trainable in an end-to-end manner, can be tested in real-time and achieves the state-of-art results for CMR correction.