Objective For the UK Biobank standardized phenotype codes are associated with patients who have been hospitalized but are missing for many patients who have been treated exclusively in an outpatient setting. We describe a method for phenotype recognition that imputes phenotype codes for all UK Biobank participants. Materials and Methods POPDx (Population-based Objective Phenotyping by Deep Extrapolation) is a bilinear machine learning framework for simultaneously estimating the probabilities of 1,538 phenotype codes. We extracted phenotypic and health-related information of 392,246 individuals from the UK Biobank for POPDx development and evaluation. A total of 12,803 ICD-10 diagnosis codes of the patients were converted to 1,538 Phecodes as gold standard labels. The POPDx framework was evaluated and compared to other available methods on automated multi-phenotype recognition. Results POPDx can predict phenotypes that are rare or even unobserved in training. We demonstrate substantial improvement of automated multi-phenotype recognition across 22 disease categories, and its application in identifying key epidemiological features associated with each phenotype. Conclusions POPDx helps provide well-defined cohorts for downstream studies. It is a general purpose method that can be applied to other biobanks with diverse but incomplete data.

Learning cross-view consistent feature representation is the key for accurate vehicle Re-identification (ReID), since the visual appearance of vehicles changes significantly under different viewpoints. To this end, most existing approaches resort to the supervised cross-view learning using extensive extra viewpoints annotations, which however, is difficult to deploy in real applications due to the expensive labelling cost and the continous viewpoint variation that makes it hard to define discrete viewpoint labels. In this study, we present a pluggable Weakly-supervised Cross-View Learning (WCVL) module for vehicle ReID. Through hallucinating the cross-view samples as the hardest positive counterparts in feature domain, we can learn the consistent feature representation via minimizing the cross-view feature distance based on vehicle IDs only without using any viewpoint annotation. More importantly, the proposed method can be seamlessly plugged into most existing vehicle ReID baselines for cross-view learning without re-training the baselines. To demonstrate its efficacy, we plug the proposed method into a bunch of off-the-shelf baselines and obtain significant performance improvement on four public benchmark datasets, i.e., VeRi-776, VehicleID, VRIC and VRAI.

Currently two barriers exist that sabotage clothing semantic parsing research: existing methods are time-consuming and the lack of large publicly available dataset that enables parsing at multiple scales. To mitigate these two dilemmas, we hereby embrace deep learning method and design a lightweight multi-scale inception neural network which is at both inside and outside multi-scale inception during training. Moreover, atrous convolution block is involved to enlarge the field of view while bringing neither extra computation cost nor parameters. Then the pre-trained model is further pruned and compressed by fine-tuning on a lightweight version of the same network used earlier, in which the inactive feature response and connections below a pre-defined threshold are directly removed. Besides, we construct so far the largest fashion guided clothing semantic parsing dataset (FCP) which contains a total of 5,000 clothing images and each image associates with both pixel-level, object-level and image-level annotations. All clothing in the dataset are recommended by fashion experts or trendsetters and contains as many as 65 common clothing items, accessories. We organize the dataset as Wordnet tree structure so that it enables fashionably parsing hierarchically. Finally, we conduct extensive experiments on three currently available datasets. Both quantitative and qualitative results demonstrate the priority and feasibility of our method, comparing with several other deep learning based methods. Our method achieves 35 FPS in a single Nvidia Titian X GPU with only minimal accuracy loss.