RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo

Learning-based multi-view stereo (MVS) has by far centered around 3D convolution on cost volumes. Due to the high computation and memory consumption of 3D CNN, the resolution of output depth is often considerably limited. Different from most existing works dedicated to adaptive refinement of cost volumes, we opt to directly optimize the depth value along each camera ray, mimicking the range finding of a laser scanner. This reduces the MVS problem to ray-based depth optimization which is much more light-weight than full cost volume optimization. In particular, we propose RayMVSNet which learns sequential prediction of a 1D implicit field along each camera ray with the zero-crossing point indicating scene depth. This sequential modeling, conducted based on transformer features, essentially learns the epipolar line search in traditional multi-view stereo. We devise a multi-task learning for better optimization convergence and depth accuracy. We found the monotonicity property of the SDFs along each ray greatly benefits the depth estimation. Our method ranks top on both the DTU and the Tanks & Temples datasets over all previous learning-based methods, achieving an overall reconstruction score of 0.33mm on DTU and an F-score of 59.48% on Tanks & Temples. It is able to produce high-quality depth estimation and point cloud reconstruction in challenging scenarios such as objects/scenes with non-textured surface, severe occlusion, and highly varying depth range. Further, we propose RayMVSNet++ to enhance contextual feature aggregation for each ray through designing an attentional gating unit to select semantically relevant neighboring rays within the local frustum around that ray. RayMVSNet++ achieves state-of-the-art performance on the ScanNet dataset. In particular, it attains an AbsRel of 0.058m and produces accurate results on the two subsets of textureless regions and large depth variation.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence. arXiv admin note: substantial text overlap with arXiv:2204.0132

The influence of the rural health security schemes on health utilization and household impoverishment in rural China: data from a household survey of western and central China

Abstract Background: The New Rural Cooperative Medical Scheme (NRCMS, voluntary health insurance) and the Medical Financial Assistance (MFA, financial relief program) were established in 2003 for rural China. The aim of this study was to document their coverage, assess their effectiveness on access to in-patient care and protection against financial catastrophe and household impoverishment due to health spending, and identify the factors predicting impoverishment with and without these schemes. Methods: A cross-sectional household survey was conducted in 2008 in Hebei and Shaanxi provinces and the Inner Mongolia Autonomous Region using a multi-stage sampling technique. Information on personal demographic characteristics, chronic illness status, health care use, household expenditure, and household health spending were collected by interview. Results: NRCMS covered 90.8% of the studied individuals and among the designated poor, 7.6% had their premiums paid by MFA. Of those referred for hospitalization in the year prior to the interview, 34.3% failed to comply, mostly (80.2%) owing to financial constraints. There was no significant difference in the unmet need for admission between the insured with NRCMS and the uninsured. Before reimbursement, the incidence of catastrophic health payment (household health spending more than 40% of household’s capacity to pay) and medical impoverishment (household per capita income falling below the poverty line due to medical expense) was 14.3% and 8.2%, respectively. NRCMS prevented 9.9% of the households from financial catastrophe and 7.7% from impoverishment, whereas MFA kept just one household from impoverishment and had no effect on financial catastrophe. Household per capita expenditure and household chronic disease proportion (proportion of members of a household with chronic illness) were the most important determinants of the unmet need for admission, risk of being impoverished and the chance of not being saved from impoverishment. Conclusion: The coverage of NRCMS among the rural population was high but not adequate to improve access to in-patient care and protect against financial catastrophe and household impoverishment due to health payment, especially for the poor and the chronically ill. Furthermore, MFA played almost no such role; therefore, the current schemes need to be improved

Effect of household and village characteristics on financial catastrophe and impoverishment due to health care spending in Western and Central Rural China: A multilevel analysis

Objective: The study aimed to examine the effect of household and community characteristics on financial catastrophe and impoverishment due to health payment in Western and Central Rural China. Methods: A household survey was conducted in 2008 in Hebei and Shaanxi provinces and the Inner Mongolia Autonomous Region using a multi-stage sampling technique. Independent variables included village characteristics, household income, chronic illness status, health care use and health spending. A composite contextual variable, named village deprivation, was derived from socio-economic status and availability of health care facilities in each village using factor analysis. Dependent variables were whether household health payment was more than 40% of household’s capacity to pay (catastrophic health payment) and whether household per capita income was put under Chinese national poverty line (1067 Yuan income per year) after health spending (impoverishment). Mixed effects logistic regression was used to assess the effect of the independent variables on the two outcomes. Results: Households with low per capita income, having elderly, hospitalized or chronically ill members, and whose head was unemployed were more likely to incur financial catastrophe and impoverishment due to health expenditure. Both catastrophic and impoverishing health payments increased with increased village deprivation. However, the presence of a village health clinic had no effect on the two outcomes, nor did household enrollment in the New Rural Cooperative Medical Scheme (national health insurance). Conclusions: Village deprivation independently increases the risk for financial hardship due to health payment after adjusting for known household-level factors. This suggests that policy makers need to view the individual, household and village as separate units for policy targeting

Early detection of gastric cancer via high-resolution terahertz imaging system

Terahertz (THz) wave has demonstrated a good prospect in recent years, but the resolution is still one of the problems that restrict the application of THz technology in medical imaging. Paraffin-embedded samples are mostly used in THz medical imaging studies, which are thicker and significantly different from the current gold standard slice pathological examination in sample preparation. In addition, THz absorption in different layers of normal and cancerous tissues also remains to be further explored. In this study, we constructed a high-resolution THz imaging system to scan non-tumorous adjacent tissue slices and gastric cancer (GC) tissue slices. In this system, a THz quantum cascade laser emitted a pulsed 3 THz signal and the transmitted THz wave was received by a THz detector implemented in a 65 nm CMOS process. The slice thickness was only 20 μm, which was close to that of the medical pathology examination. We successfully found THz transmittance differences between different layers of normal gastric tissues based on THz images, and the resolution could reach 60 μm for the first time. The results indicated that submucosa had a lower THz transmittance than that of mucosa and muscular layer in non-tumorous adjacent tissue. However, in GC tissue, THz transmittance of mucosa and submucosa was similar, caused by the decreased transmittance of mucosa, where the cancer occurs. Therefore, we suppose that the similar terahertz transmittance between gastric mucosa and submucosa may indicate the appearance of cancerization. The images obtained from our THz imaging system were clearer than those observed with naked eyes, and can be directly compared with microscopic images. This is the first application of THz imaging technology to identify non-tumorous adjacent tissue and GC tissue based on the difference in THz wave absorption between different layers in the tissue. Our present work not only demonstrated the potential of THz imaging to promote early diagnosis of GC, but also suggested a new direction for the identification of normal and cancerous tissues by analyzing differences in THz transmittance between different layers of tissue

Identifying Subgroups of ICU Patients Using End-to-End Multivariate Time-Series Clustering Algorithm Based on Real-World Vital Signs Data

This study employed the MIMIC-IV database as data source to investigate the use of dynamic, high-frequency, multivariate time-series vital signs data, including temperature, heart rate, mean blood pressure, respiratory rate, and SpO2, monitored first 8 hours data in the ICU stay. Various clustering algorithms were compared, and an end-to-end multivariate time series clustering system called Time2Feat, combined with K-Means, was chosen as the most effective method to cluster patients in the ICU. In clustering analysis, data of 8,080 patients admitted between 2008 and 2016 was used for model development and 2,038 patients admitted between 2017 and 2019 for model validation. By analyzing the differences in clinical mortality prognosis among different categories, varying risks of ICU mortality and hospital mortality were found between different subgroups. Furthermore, the study visualized the trajectory of vital signs changes. The findings of this study provide valuable insights into the potential use of multivariate time-series clustering systems in patient management and monitoring in the ICU setting.Comment: Proceedings of Beijing Health Data Science Summit (HDSS) 202