MetaAge: Meta-Learning Personalized Age Estimators

Different people age in different ways. Learning a personalized age estimator for each person is a promising direction for age estimation given that it better models the personalization of aging processes. However, most existing personalized methods suffer from the lack of large-scale datasets due to the high-level requirements: identity labels and enough samples for each person to form a long-term aging pattern. In this paper, we aim to learn personalized age estimators without the above requirements and propose a meta-learning method named MetaAge for age estimation. Unlike most existing personalized methods that learn the parameters of a personalized estimator for each person in the training set, our method learns the mapping from identity information to age estimator parameters. Specifically, we introduce a personalized estimator meta-learner, which takes identity features as the input and outputs the parameters of customized estimators. In this way, our method learns the meta knowledge without the above requirements and seamlessly transfers the learned meta knowledge to the test set, which enables us to leverage the existing large-scale age datasets without any additional annotations. Extensive experimental results on three benchmark datasets including MORPH II, ChaLearn LAP 2015 and ChaLearn LAP 2016 databases demonstrate that our MetaAge significantly boosts the performance of existing personalized methods and outperforms the state-of-the-art approaches.Comment: Accepted by IEEE Transactions on Image Processing (TIP

Single-Sample Finger Vein Recognition via Competitive and Progressive Sparse Representation

As an emerging biometric technology, finger vein recognition has attracted much attention in recent years. However, single-sample recognition is a practical and longstanding challenge in this field, referring to only one finger vein image per class in the training set. In single-sample finger vein recognition, the illumination variations under low contrast and the lack of information of intra-class variations severely affect the recognition performance. Despite of its high robustness against noise and illumination variations, sparse representation has rarely been explored for single-sample finger vein recognition. Therefore, in this paper, we focus on developing a new approach called Progressive Sparse Representation Classification (PSRC) to address the challenging issue of single-sample finger vein recognition. Firstly, as residual may become too large under the scenario of single-sample finger vein recognition, we propose a progressive strategy for representation refinement of SRC. Secondly, to adaptively optimize progressions, a progressive index called Max Energy Residual Index (MERI) is defined as the guidance. Furthermore, we extend PSRC to bimodal biometrics and propose a Competitive PSRC (C-PSRC) fusion approach. The C-PSRC creates more discriminative fused sample and fusion dictionary by comparing residual errors of different modalities. By comparing with several state-of-the-art methods on three finger vein benchmarks, the superiority of the proposed PSRC and C-PSRC is clearly demonstrated

Automated diagnosis of pancreatic mucinous and serous cystic neoplasms with modality-fusion deep neural network using multi-modality MRIs

BackgroundPancreatic cystic neoplasms are increasingly diagnosed with the development of medical imaging technology and people’s self-care awareness. However, two of their sub-types, serous cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN), are often misclassified from each other. Because SCN is primarily benign and MCN has a high rate of malignant transformation. Distinguishing SCN and MCN is challenging and essential.PurposeMRIs have many different modalities, complete with SCN and MCN diagnosis information. With the help of an artificial intelligence-based algorithm, we aimed to propose a multi-modal hybrid deep learning network that can efficiently diagnose SCN and MCN using multi-modality MRIs.MethodsA cross-modal feature fusion structure was innovatively designed, combining features of seven modalities to realize the classification of SCN and MCN. 69 Patients with multi-modalities of MRIs were included, and experiments showed performances of every modality.ResultsThe proposed method with the optimized settings outperformed all other techniques and human radiologists with high accuracy of 75.07% and an AUC of 82.77%. Besides, the proposed disentanglement method outperformed other fusion methods, and delayed contrast-enhanced T1-weighted MRIs proved most valuable in diagnosing SCN and MCN.ConclusionsThrough the use of a contemporary artificial intelligence algorithm, physicians can attain high performance in the complex challenge of diagnosing SCN and MCN, surpassing human radiologists to a significant degree

Fat-mass and obesity-associated gene polymorphisms and weight gain after risperidone treatment in first episode schizophrenia

BACKGROUND: Obesity induced by antipsychotics severely increases the risk of many diseases and significantly reduces quality of life. Genome Wide Association Studies has identified fat-mass and obesity-associated (FTO) gene associated with obesity. The relationship between the FTO gene and drug-induced obesity is unclear. METHOD: Two hundred and fifty drug naive, Chinese Han patients with first-episode schizophrenia were enrolled in the study, and genotyped for four single nucleotide polymorphisms (SNPs rs9939609, rs8050136, rs1421085 and rs9930506) by the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and direct sequencing. Body weight and body mass index (BMI) were measured at baseline and six months after risperidone treatment. RESULTS: At baseline, body weight and BMI of TT homozygotes were lower than those of A allele carriers in rs9939609; body weight of AA homozygotes was higher than those of G allele carriers in rs9930506 (p\u27s \u3c 0.05). After 6 months of risperidone treatment, body weight and BMI of TT homozygotes were lower than those of A allele carriers in rs9939609 (p\u27s \u3c 0.01); body weight and BMI of CC homozygotes were lower than those of A allele carriers in rs8050136 (p\u27s \u3c 0.05); body weight of AA homozygotes was higher than those of G allele carriers in rs9930506 (p\u27s \u3c 0.05). After controlling for age, gender, age of illness onset, disease duration, weight at baseline and education, weight gain of TT homozygotes at 6 months remained to be lower than those of A allele carriers in rs9939609 (p \u3c 0.01); weight gain of CC homozygotes at 6 months was lower than those of A allele carriers in rs8050136 (p = 0.01). Stepwise multiple regression analysis suggested that, among 4 SNPs, rs9939609 was the strongest predictor of weight gain after 6 months of risperidone treatment (p = 0.001). CONCLUSIONS: The FTO gene polymorphisms, especially rs9939609, seem to be related to weight gain after risperidone treatment in Chinese Han patients with first episode schizophrenia