Bringing contribution–receipt (im)balance to team–member exchange research: A moderated mediation model

Liu, Xin; Zhang, Yucheng; Chen, Wansi; Li, Wen Dong; Ni, Dan

doi:10.1002/job.2686

Cited by 3 publications

(1 citation statement)

References 117 publications

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“…This illustrates the potential of CLIP models when applied to more comprehensive and varied datasets. 223 The application of emerging innovative techniques, such as SAM and CLIP, to large-scale medical datasets has the potential to revolutionize medical image analysis. Technical progress may be the key to overcoming the limitations of traditional deep learning methods and enabling more efficient, accurate, and generalizable models in various data availability conditions.…”

Section: Summary and Discussionmentioning

confidence: 99%

A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation

Liu,

Kainth,

Zhou

et al. 2024

NMR in Biomedicine

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Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with applications in disease diagnostics, intervention, and treatment planning. Accurate MRI segmentation is critical for diagnosing abnormalities, monitoring diseases, and deciding on a course of treatment. With the advent of advanced deep learning frameworks, fully automated and accurate MRI segmentation is advancing. Traditional supervised deep learning techniques have advanced tremendously, reaching clinical‐level accuracy in the field of segmentation. However, these algorithms still require a large amount of annotated data, which is oftentimes unavailable or impractical. One way to circumvent this issue is to utilize algorithms that exploit a limited amount of labeled data. This paper aims to review such state‐of‐the‐art algorithms that use a limited number of annotated samples. We explain the fundamental principles of self‐supervised learning, generative models, few‐shot learning, and semi‐supervised learning and summarize their applications in cardiac, abdomen, and brain MRI segmentation. Throughout this review, we highlight algorithms that can be employed based on the quantity of annotated data available. We also present a comprehensive list of notable publicly available MRI segmentation datasets. To conclude, we discuss possible future directions of the field—including emerging algorithms, such as contrastive language‐image pretraining, and potential combinations across the methods discussed—that can further increase the efficacy of image segmentation with limited labels.

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Section: Summary and Discussionmentioning

confidence: 99%

A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation

Liu,

Kainth,

Zhou

et al. 2024

NMR in Biomedicine

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The effect of leader-team (in-)congruence in future orientation on leader’s participative leadership: the moderating role of leader workload

Xu,

Huang,

Zhao

2024

CMS

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Purpose Prior studies have consistently treated participative leadership as a given leadership style. Conversely, this study aims to prove that participative leadership can be predicted by leaders and teams collectively, depending on leaders' work characteristics (i.e. workload). Design/methodology/approach A two-source survey was designed to collect data from a sample of 89 leader-team dyads in a trading company in a southeastern Chinese city. Polynomial regression and response surface analysis were used to test the hypotheses. Findings The leader showed more participative leadership when leader-team future orientation was congruent rather than incongruent; in the congruent situation, there was an inverted U-shaped relationship between leader-team future orientation congruence and participative leadership; in the incongruent situation, when the team's future orientation gradually exceeded the leader's, participative leadership first increased and then decreased; and leader workload positively moderated the relationship between leader-team future orientation congruence and participative leadership. Originality/value These findings theoretically respond to the call for investigating the influence of leader-team future orientation congruence on leaders’ behaviors, and in practice enlighten managers on how to encourage supervisors to involve employees in decision-making processes.

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Personality development goals at work: Would a new assessment tool help?

Li,

Hu,

Wang

2024

Int J Selection Assessment

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We commend the focal article by Dupré and Wille (2024) that introduces personality development goals at work. Yet, to many organizational researchers, this may strike as a bold proposal given its novelty and provocative nature. Seeing the potential of this proposal, we offer discussions on potential theoretical and methodological challenges that researchers, who are eager to advance this line of research, may encounter. We encourage future research to tackle these issues in order to further advance theoretical developments and practical applications of personality development at work.

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Bringing contribution–receipt (im)balance to team–member exchange research: A moderated mediation model

Cited by 3 publications

References 117 publications

A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation

A review of self‐supervised, generative, and few‐shot deep learning methods for data‐limited magnetic resonance imaging segmentation

The effect of leader-team (in-)congruence in future orientation on leader’s participative leadership: the moderating role of leader workload

Personality development goals at work: Would a new assessment tool help?

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