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IEEE Transactions on Pattern Analysis... Jun 2024Segmenting unknown or anomalous object instances is a critical task in autonomous driving applications, and it is approached traditionally as a per-pixel classification...
Segmenting unknown or anomalous object instances is a critical task in autonomous driving applications, and it is approached traditionally as a per-pixel classification problem. However, reasoning individually about each pixel without considering their contextual semantics results in high uncertainty around the objects' boundaries and numerous false positives. We propose a paradigm change by shifting from a per-pixel classification to a mask classification. Our mask-based method, Mask2Anomaly, demonstrates the feasibility of integrating a mask-classification architecture to jointly address anomaly segmentation, open-set semantic segmentation, and open-set panoptic segmentation. Mask2Anomaly includes several technical novelties that are designed to improve the detection of anomalies/unknown objects: i) a global masked attention module to focus individually on the foreground and background regions; ii) a mask contrastive learning that maximizes the margin between an anomaly and known classes; iii) a mask refinement solution to reduce false positives; and iv) a novel approach to mine unknown instances based on the mask- architecture properties. By comprehensive qualitative and qualitative evaluation, we show Mask2Anomaly achieves new state-of-the-art results across the benchmarks of anomaly segmentation, open-set semantic segmentation, and open-set panoptic segmentation. The code and pre-trained models are available: https://github.com/shyam671/Mask2Anomaly-Unmasking-Anomalies-in-Road-Scene-Segmentation/tree/main.
PubMed: 38935478
DOI: 10.1109/TPAMI.2024.3419055 -
IEEE Transactions on Pattern Analysis... Jun 2024The U-Net-like coarse-to-fine network design is currently the dominant choice for dense prediction tasks. Although this design can often achieve competitive performance,...
The U-Net-like coarse-to-fine network design is currently the dominant choice for dense prediction tasks. Although this design can often achieve competitive performance, it suffers from some inherent limitations, such as training error propagation from low to high resolution and the dependency on the deeper and heavier backbones. To design an effective network that performs better, we instead propose Recurrent Multiscale Feature Modulation (R-MSFM), a new lightweight network design for self-supervised monocular depth estimation. R-MSFM extracts per-pixel features, builds a multiscale feature modulation module, and performs recurrent depth refinement through a parameter-shared decoder at a fixed resolution. This network design enables our R-MSFM to maintain a more lightweight architecture and fundamentally avoid error propagation caused by the coarse-to-fine design. Furthermore, we introduce the mask geometry consistency loss to facilitate our R-MSFM for geometry consistent depth learning. This loss penalizes the inconsistency of the estimated depths between adjacent views within the nonoccluded and nonstationary regions. Experimental results demonstrate the superiority of our proposed R-MSFM both at model size and inference speed, and show state-of-the-art results on two datasets: KITTI and Make3D. The code is available at https://github.com/jsczzzk/R-MSFM.
PubMed: 38935477
DOI: 10.1109/TPAMI.2024.3420165 -
Cognitive Research: Principles and... Jun 2024The presence of face masks can significantly impact processes related to trait impressions from faces. In the present research, we focused on trait impressions from...
The presence of face masks can significantly impact processes related to trait impressions from faces. In the present research, we focused on trait impressions from faces either wearing a mask or not by addressing how contextual factors may shape such inferences. In Study 1, we compared trait impressions from faces in a phase of the COVID-19 pandemic in which wearing masks was a normative behavior (T1) with those assessed one year later when wearing masks was far less common (T2). Results at T2 showed a reduced positivity in the trait impressions elicited by faces covered by a mask. In Study 2, it was found that trait impressions from faces were modulated by the background visual context in which the target face was embedded so that faces wearing a mask elicited more positive traits when superimposed on an indoor rather than outdoor visual context. Overall, the present studies indicate that wearing face masks may affect trait impressions from faces, but also that such impressions are highly flexible and can significantly fluctuate across time and space.
Topics: Humans; Masks; Female; Male; COVID-19; Adult; Young Adult; Facial Recognition; Social Perception; Facial Expression
PubMed: 38935222
DOI: 10.1186/s41235-024-00570-w -
Indian Journal of Public Health Oct 2023The augmentation of precautionary behaviors through the application of health belief model (HBM) constructs could help in curbing the current pandemic.
BACKGROUND
The augmentation of precautionary behaviors through the application of health belief model (HBM) constructs could help in curbing the current pandemic.
OBJECTIVES
The objectives are to assess adherence to CAB and to evaluate its predictors using the constructs of HBM among COVID-19 vaccinees in Himachal Pradesh.
MATERIALS AND METHODS
A cross-sectional study using a telephone survey, with two-step stratified random sampling, was employed to acquire a sample of 441 respondents from Himachal Pradesh. Formal interviews were conducted using pretested, structured, self-administered questionnaires.
RESULTS
The mean age of respondents was 32.16 years (standard deviation = 12.77; range = 18-78 years). Maximum adherence was seen for wearing masks at 83% (95% confidence interval [CI]: 79.3%-86.3%), followed by maintenance of respiratory hygiene at 72.3% (95% CI 68%-78.4%). Nearly 42.2% (95% CI 37.6%-47.8%) conformed to social distancing norms. We observed minimum adherence for handwashing practices of 12.9% (95% CI 10%-16.3.0%). On bivariate analyses, except for perceived severity, all HBM constructs were significantly associated with CAB. However, after adjusting for gender, age, education, area of residence, and reduced income in multivariate analysis, perceived susceptibility, perceived barriers, and exposure to cues to action remained significant predictors of CAB.
CONCLUSIONS
The study highlights the empirical evidence of the application of HBM constructs to enhance behavioral adherence to COVID-19 precautionary measures.
Topics: Humans; COVID-19; India; Adult; Cross-Sectional Studies; Male; Female; Middle Aged; Health Belief Model; Adolescent; Young Adult; Aged; SARS-CoV-2; Health Behavior; Health Knowledge, Attitudes, Practice; Socioeconomic Factors; Hand Disinfection
PubMed: 38934832
DOI: 10.4103/ijph.ijph_1525_22 -
Indian Journal of Public Health Oct 2023National Strategic Plan to End tuberculosis (TB) in India 2020-2025 aims to prevent the emergence of TB in susceptible populations. Airborne infection control (AIC)...
BACKGROUND
National Strategic Plan to End tuberculosis (TB) in India 2020-2025 aims to prevent the emergence of TB in susceptible populations. Airborne infection control (AIC) practices in high-risk settings like homes for the aged (HFA) will be essential to achieve this.
OBJECTIVE
The objective is to assess the AIC practices (AICPs) in HFA in the Kollam district in Kerala, India.
MATERIALS AND METHODS
A mixed method approach was used. the study was done in five HFA s in a southern district of Kerala to find AICPs. Using purposive sampling, in-depth interviews was conducted among inmates with recent respiratory infection and administrators. Environmental measures were assessed using an observation checklist.
RESULTS
Ventilation was inadequate in 25%-40% of HFA. Air change per hour and distance between beds were less. Very few inmates were aware of the need for proper ventilation and personal hygiene. Wearing masks and hand hygiene was not practiced. Administrators faced shortages of space, funds, and human resources for caring for hospitalized inmates, and psychiatric and terminally ill patients.
CONCLUSIONS
There is a need to train the staff and inmates on AIC. Infrastructural improvements, like the use of partition screens in the short term and the creation of model airborne infection control HFA in the long run, with a collaborative effort from health professionals and architects, are needed for TB elimination efforts to succeed.
Topics: Humans; India; Ventilation; Infection Control; Tuberculosis; Homes for the Aged; Interviews as Topic; Male
PubMed: 38934828
DOI: 10.4103/ijph.ijph_912_22 -
Indian Journal of Dental Research :... Jan 2024This case report presents a rare combination of congenital anomalies in an otherwise healthy male infant born at 36 weeks. The infant was diagnosed with congenital...
RATIONALE
This case report presents a rare combination of congenital anomalies in an otherwise healthy male infant born at 36 weeks. The infant was diagnosed with congenital maxillomandibular synechia, ectrodactyly, and ankyloglossia superior syndrome (ASS).
PATIENT CONCERNS
Inability to open the mouth completely, feeding challenges, and a cleft palate. The infant was stabilized through successful positive pressure ventilation via a face mask at birth and enteral feeding was initiated via a feeding gastrostomy.
EXAMINATION
Diagnostic tests revealed a midline palatal cleft, hypoplastic jaws, persistent metopic suture, and a bony fusion at the midline.
TREATMENT
Sectioning of the bony spur along the midline and achieving a mouth opening of 2 cm post-manipulation. The patient is under follow-up, with future treatment plans including cleft palate correction at 12 months and potential frontomandibular and lower jaw advancement depending on growth trajectories.
TAKEAWAY LESSONS
This case underscores the complexity of managing multiple congenital anomalies and the need for individualized treatment plans.
Topics: Humans; Male; Cleft Palate; Tongue; Palate, Hard; Infant, Newborn; Abnormalities, Multiple; Maxilla; Ankyloglossia; Jaw Abnormalities; Mandible
PubMed: 38934763
DOI: 10.4103/ijdr.ijdr_961_23 -
Bulletin of the World Health... Jul 2024To examine how a general inpatient satisfaction survey functions as a hospital performance measure.
OBJECTIVE
To examine how a general inpatient satisfaction survey functions as a hospital performance measure.
METHODS
We conducted a mixed-methods pilot study of the Hospital Consumer Assessment of Health Providers and Systems survey in Odisha, India. We divided the study into three steps: cognitive testing of the survey, item testing with exploratory factor analysis and content validity indexing. Cognitive testing involved 50 participants discussing their interpretation of survey items. The survey was then administered to 507 inpatients across five public hospitals in Odisha, followed by exploratory factor analysis. Finally, we interviewed 15 individuals to evaluate the content validity of the survey items.
FINDINGS
Cognitive testing revealed that six out of 18 survey questions were not consistently understood within the Odisha inpatient setting, highlighting issues around responsibilities for care. Exploratory factor analysis identified a six-factor structure explaining 66.7% of the variance. Regression models showed that interpersonal care from doctors and nurses had the strongest association with overall satisfaction. An assessment of differential item functioning revealed that patients with a socially marginalized caste reported higher disrespectful care, though this did not translate into differences in reported satisfaction. Content validity indexing suggested that discordance between experiences of disrespectful care and satisfaction ratings might be due to low patient expectations.
CONCLUSION
Using satisfaction ratings without nuanced approaches in value-based purchasing programmes may mask poor-quality interpersonal services, particularly for historically marginalized patients. Surveys should be designed to accurately capture true levels of dissatisfaction, ensuring that patient concerns are not hidden.
Topics: Humans; India; Patient Satisfaction; Female; Male; Adult; Middle Aged; Value-Based Purchasing; Pilot Projects; Surveys and Questionnaires; Hospitals, Public; Factor Analysis, Statistical; Quality of Health Care; Young Adult
PubMed: 38933484
DOI: 10.2471/BLT.24.290519 -
Frontiers in Oncology 2024Precise segmentation of Odontogenic Cystic Lesions (OCLs) from dental Cone-Beam Computed Tomography (CBCT) is critical for effective dental diagnosis. Although...
OBJECTIVES
Precise segmentation of Odontogenic Cystic Lesions (OCLs) from dental Cone-Beam Computed Tomography (CBCT) is critical for effective dental diagnosis. Although supervised learning methods have shown practical diagnostic results in segmenting various diseases, their ability to segment OCLs covering different sub-class varieties has not been extensively investigated.
METHODS
In this study, we propose a new supervised learning method termed OCL-Net that combines a Multi-Scaled U-Net model, along with an Auto-Adapting mechanism trained with a combined supervised loss. Anonymous CBCT images were collected retrospectively from one hospital. To assess the ability of our model to improve the diagnostic efficiency of maxillofacial surgeons, we conducted a diagnostic assessment where 7 clinicians were included to perform the diagnostic process with and without the assistance of auto-segmentation masks.
RESULTS
We collected 300 anonymous CBCT images which were manually annotated for segmentation masks. Extensive experiments demonstrate the effectiveness of our OCL-Net for CBCT OCLs segmentation, achieving an overall Dice score of 88.84%, an IoU score of 81.23%, and an AUC score of 92.37%. Through our diagnostic assessment, we found that when clinicians were assisted with segmentation labels from OCL-Net, their average diagnostic accuracy increased from 53.21% to 55.71%, while the average time spent significantly decreased from 101s to 47s (P<0.05).
CONCLUSION
The findings demonstrate the potential of our approach as a robust auto-segmentation system on OCLs in CBCT images, while the segmented masks can be used to further improve OCLs dental diagnostic efficiency.
PubMed: 38933446
DOI: 10.3389/fonc.2024.1379624 -
Cureus Jun 2024Although rare, acute compartment syndrome may develop as a simple elbow dislocation after reduction without initial motor, sensory, or peripheral circulatory...
Although rare, acute compartment syndrome may develop as a simple elbow dislocation after reduction without initial motor, sensory, or peripheral circulatory abnormalities. This report describes a rare case of this condition. Acute compartment syndrome remains a potential complication, even in a simple elbow dislocation without apparent initial abnormalities, and should be explained to patients. A peripheral nerve block during reduction may mask symptoms and delay recognition of acute compartment syndrome. This case highlights the importance of vigilant monitoring for acute compartment syndrome following reduction of simple elbow dislocations, especially when a peripheral nerve block is used during reduction.
PubMed: 38933345
DOI: 10.7759/cureus.63145 -
Frontiers in Medicine 2024The aim of this study is to determine the steps of a training program utilizing Head-Mounted Display (HMD) based Virtual Reality Technology to enhance nursing students'...
Designing and implementing a training program on surgical hand scrubbing, wearing surgical cap and surgical mask, gowning, and gloving using HMD-based virtual reality technologies for nursing students: an exploration of student perceptions.
OBJECTIVE
The aim of this study is to determine the steps of a training program utilizing Head-Mounted Display (HMD) based Virtual Reality Technology to enhance nursing students' skills in surgical hand scrubbing, wearing surgical cap and surgical mask, gowning and gloving, and to evaluate students' perceptions toward the program.
METHODS
The study aimed to investigate the potential applications of HMD-Based Virtual Reality Technology in Surgical Hand Scrubbing, Wearing Surgical Cap and Surgical Mask, Gowning and Gloving Program for nursing students, as well as students' perceptions toward this technology. The research was conducted with a focus group consisting of second-year nursing students in Osmaniye/Turkey, between January and June 2022, and the training program was implemented in five stages: Analysis, Design, Development, Implementation, and Evaluation. The program was evaluated with a focus group of nursing students. Focus group discussions were conducted to provide insights into students' experiences, feedback, and perceptions of the program.
RESULTS
A vast majority of participants (92.5%) reported feeling fully immersed in the operating room environment during the virtual reality (VR) experience. Notably, all students acknowledged the potential of HMD-Based Virtual Reality Technology to enrich their understanding of surgical hand scrubbing, wearing surgical cap and surgical mask, gowning and gloving procedures, surpassing conventional instructional models. While many participants found the experience exhilarating (85.1%), a considerable portion reported a decline in engagement after repeated exposures (88.8%). Overall, participants welcomed the integration of VR technology into education, expressing optimism about its capacity to facilitate additional instructional modules (74.4%). Moreover, they conveyed satisfaction with the opportunity to engage with the VR application, emphasizing its significant educational value (81.4%).
CONCLUSION
Based on these findings, we can suggest that virtual reality technology has the potential to have an impact on nursing students' education. The majority of students expressing a sense of presence in the operating room highlights the value of this method in education. However, the reported boredom after repeated experiences by most participants underscores the importance of diversifying the program and introducing innovative approaches to keep students engaged.
PubMed: 38933115
DOI: 10.3389/fmed.2024.1364465