An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data

In medical studies, the collected covariates usually contain underlying outliers. For clustered /longitudinal data with censored observations, the traditional Gehan-type estimator is robust to outliers existing in response but sensitive to outliers in the covariate domain, and it also ignores the within-cluster correlations. To take account of within-cluster correlations, varying cluster sizes, and outliers in covariates, we propose weighted Gehan-type estimating functions for parameter estimation in the accelerated failure time model for clustered data. We provide the asymptotic properties of the resulting estimators and carry out simulation studies to evaluate the performance of the proposed method under a variety of realistic settings. The simulation results demonstrate that the proposed method is robust to the outliers existing in the covariate domain and lead to much more efficient estimators when a strong within-cluster correlation exists. Finally, the proposed method is applied to a medical dataset and more reliable and convincing results are hence obtained.Comment: ready for submissio

UniHPE: Towards Unified Human Pose Estimation via Contrastive Learning

In recent times, there has been a growing interest in developing effective perception techniques for combining information from multiple modalities. This involves aligning features obtained from diverse sources to enable more efficient training with larger datasets and constraints, as well as leveraging the wealth of information contained in each modality. 2D and 3D Human Pose Estimation (HPE) are two critical perceptual tasks in computer vision, which have numerous downstream applications, such as Action Recognition, Human-Computer Interaction, Object tracking, etc. Yet, there are limited instances where the correlation between Image and 2D/3D human pose has been clearly researched using a contrastive paradigm. In this paper, we propose UniHPE, a unified Human Pose Estimation pipeline, which aligns features from all three modalities, i.e., 2D human pose estimation, lifting-based and image-based 3D human pose estimation, in the same pipeline. To align more than two modalities at the same time, we propose a novel singular value based contrastive learning loss, which better aligns different modalities and further boosts the performance. In our evaluation, UniHPE achieves remarkable performance metrics: MPJPE $50.5$mm on the Human3.6M dataset and PAMPJPE $51.6$mm on the 3DPW dataset. Our proposed method holds immense potential to advance the field of computer vision and contribute to various applications

Back to Optimization: Diffusion-based Zero-Shot 3D Human Pose Estimation

Learning-based methods have dominated the 3D human pose estimation (HPE) tasks with significantly better performance in most benchmarks than traditional optimization-based methods. Nonetheless, 3D HPE in the wild is still the biggest challenge of learning-based models, whether with 2D-3D lifting, image-to-3D, or diffusion-based methods, since the trained networks implicitly learn camera intrinsic parameters and domain-based 3D human pose distributions and estimate poses by statistical average. On the other hand, the optimization-based methods estimate results case-by-case, which can predict more diverse and sophisticated human poses in the wild. By combining the advantages of optimization-based and learning-based methods, we propose the Zero-shot Diffusion-based Optimization (ZeDO) pipeline for 3D HPE to solve the problem of cross-domain and in-the-wild 3D HPE. Our multi-hypothesis ZeDO achieves state-of-the-art (SOTA) performance on Human3.6M as minMPJPE $51.4$mm without training with any 2D-3D or image-3D pairs. Moreover, our single-hypothesis ZeDO achieves SOTA performance on 3DPW dataset with PA-MPJPE $42.6$mm on cross-dataset evaluation, which even outperforms learning-based methods trained on 3DPW

Prognostic impact of the Controlling Nutritional Status Score in patients with biliary tract cancer: a systematic review and meta-analysis

BackgroundBiliary tract cancer (BTC) is a malignancy associated with unfavorable outcomes. Advanced BTC patients have a propensity to experience compromised immune and nutritional status as a result of obstructive jaundice and biliary inflammation. Currently, there is a lack of consensus on the impact of the Controlling Nutritional Status (CONUT) score in the context of BTC prognosis. The purpose of this study is to conduct a meta-analysis on the association between CONUT and the prognosis of patients suffering from BTC.MethodsA defined search strategy was implemented to search the PubMed, Embase, and Web of Science databases for eligible studies published until March 2023, with a focus on overall survival (OS), relapse-free survival/recurrence-free survival(RFS), and relevant clinical characteristics. The prognostic potential of the CONUT score was evaluated using hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs).ResultsIn this meta-analysis, a total of 1409 patients from China and Japan were involved in 9 studies. The results indicated that the CONUT score was significantly correlated with worse OS (HR=2.13, 95% CI 1.61-2.82, P<0.0001) and RFS (HR=1.83, 95% CI 1.44–2.31, P<0.0001) in patients with BTC. And, the analysis showed that a high CONUT score was significantly associated with clinical characteristics such as jaundice (OR=1.60, 95% CI=1.14–2.25, P=0.006), poorly differentiated tumor (OR=1.43, 95% CI=1.03–1.99, P=0.03), pT3 and 4 stage of the tumor (OR=1.87, 95% CI=1.30–2.68, P=0.0007), and complications of Clavien-Dindo classification grade IIIa or higher (OR=1.79, 95% CI=1.03–3.12, P=0.04).ConclusionThis meta-analysis indicates that a high CONUT score can serve as a significant prognostic indicator for survival outcomes among patients diagnosed with BTC

Non-invasive color imaging through scattering medium under broadband illumination

Due to the complex of mixed spectral point spread function within memory effect range, it is unreliable and slow to use speckle correlation technology for non-invasive imaging through scattering medium under broadband illumination. The contrast of the speckles will drastically drop as the light source's spectrum width increases. Here, we propose a method for producing the optical transfer function with several speckle frames within memory effect range to image under broadband illumination. The method can be applied to image amplitude and color objects under white LED illumination. Compared to other approaches of imaging under broadband illumination, such as deep learning and modified phase retrieval, our method can provide more stable results with faster convergence speed, which can be applied in high speed scattering imaging under natural light illumination