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Leadership and Sustainability in a Globalized World: Emerging                                                              A systematic review of machine learning for digital stain
         Trends, Global Contexts, Key Challenges, and Effective Strategies                                                                                      processing in pathology








                                                       Author(s):                                                                                                             Author(s):


        Javed, M. (Rabat Business School, Department of Management Sciences, International University of Rabat; COMSATS        Al-Qudah, R. (Abu Dhabi University, College of Engineering, Department of Computer Science and Information
        University Islamabad, Sahiwal Campus), Bilal, B. (Durham University Business School, Durham University; Department     Technology), Bala, A. (Monash University Malaysia, School of Business), AlMuhajiri, M. (Saudi Electronic University,
        of Financial Intelligence, University of South Africa), Mouri, N. (Rabat Business School, Department of Management     College of Computing and Informatics), Zakaria, K. (Abu Dhabi University, College of Engineering, Department of
                 Sciences, International University of Rabat), Faisal, M. (College of Business, Abu Dhabi University)          Computer Science and Information Technology), Suen, C.Y. (Concordia University, Gina Cody School of Engineering
                                                                                                                                                                         and Computer Science)
                                                     Index Terms:
                                                                                                                                                                             Index Terms:
                   climate action; gender equality; grand societal challenges; innovation; leadership; sustainability
                                                                                                                               Chemical equipment; E-learning; Generative adversarial networks; Learning systems; Pathology; Reagents; Adversarial
                                                        Abstract:                                                              networks; Chemical method; Chemical staining; Digital pathologies; Digital staining; Machine-learning; Skilled personnel;
                                                                                                                               Systematic Review; Toxic reagents; Virtual staining; artificial intelligence; augmentation index; chemical analysis; dark-

        This systematic review analyzes 109 studies from top business journals, exploring the connection between leadership    field microscopy; deep learning; generative adversarial network; Giemsa stain; histopathology; human; image analysis;
        and sustainability, including key leadership styles, emerging trends, and challenges in integrating sustainability into   immunohistochemistry; machine learning; nonhuman; pathology; perception; Review; signal noise ratio; stain; systematic
        organizational strategies and operations. Our analysis reveals responsible leadership, followed by transformational                                    review; taxonomy; training; workflow; Surveying
        and female leadership, as the most common styles used to drive the integration of sustainability into organizational
        strategies  and  operations.  Core  sustainability  practices  are  more  closely  aligned  with  addressing  grand  societal                                          Abstract:
        challenges, particularly “climate action” followed by “quality education,” “innovation,” and “gender equality.” The study
        highlights key challenges facing the leadership in driving sustainability include stakeholders’ resistance to change,
        financial constraints, capacity building, and regulatory hurdles. Overcoming these requires raising stakeholder        Digital staining involves using methods such as Machine Learning (ML) to replace chemical staining in pathology.
        awareness,  aligning  sustainability  with  business  goals,  leveraging  cross-sectoral  collaboration,  and  regulatory   Staining adds contrast that makes cell details more visible under the microscope. However, chemical methods are
        harmonization. This study highlights the transformative role of leadership in promoting sustainability, emphasizing    slow, use toxic reagents, and require skilled personnel. In contrast, digital staining can generate images faster, reduce
                                     cross-sectoral learning to drive meaningful change.                                       the need for reagents and specialized equipment, and minimize plastic and chemical waste, making the workflow more
                                                                                                                               sustainable. This paper systematically reviews papers published on ML-based digital stain processing. We propose a
                                                    Read the paper                                                             new taxonomy that divides existing studies into five groups: stain normalization, stain augmentation, virtual staining,
                                                                                                                               stain transformation, and hybrid approaches. In addition, we observed several trends from the reviewed papers.
                                                                                                                                                                Finally, we outline open research directions.


                                                                                                                                                                           Read the paper














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