WITHDRAWN: A Semi-Convolutional Framework for Effective Instance Segmentation of Melanoma in Dermatological Images Using Spatially Enhanced Embedding Techniques

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Abstract

Abstract Melanoma segmentation has traditionally been addressed using region-based methods such as Mask RCNN, which are effective but often limited by their reliance on bounding box approximations. Recently, there has been a growing shift towards pixel-level segmentation tasks, as they offer the potential for greater precision and more seamless integration into end-to-end image-to-image frameworks. This paper explores the theoretical and empirical challenges of achieving instance-level melanoma segmentation through dense pixel embeddings, highlighting the limitations of conventional convolutional operators in isolating distinct instances. We propose semi-convolutional modifications, which enhance the capability of the model to distinguish between different instances of melanoma, by adapting Hough voting techniques and introducing a variant of the bilateral kernel that is spatially guided by a convolutional network. Our findings show that these novel operators not only improve the accuracy of melanoma segmentation but also outperform traditional region-based methods like Mask RCNN, especially in handling the complex and irregular shapes of melanomas, demonstrating significant improvements over previous techniques in both biological and medical imaging tasks.
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WITHDRAWN: A Semi-Convolutional Framework for Effective Instance Segmentation of Melanoma in Dermatological Images Using Spatially Enhanced Embedding Techniques | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article WITHDRAWN: A Semi-Convolutional Framework for Effective Instance Segmentation of Melanoma in Dermatological Images Using Spatially Enhanced Embedding Techniques Ravi Kumar, Priya Sharma, Aakash Patel, Saanvi Reddy, Soo-Jung Kim, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7470134/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Editorial Note 4 September, 2025. Research Square has withdrawn this preprint due to significant concerns regarding the integrity of the work. Maintaining high standards of ethical conduct is essential to our platform. Editorial notes are used to provide important context regarding the topic of a preprint or to alert readers to potential issues concerning that preprint or a downstream publication associated with it. For more information on editorial notes, see our Editorial Policies . Abstract 4 September, 2025. Research Square has withdrawn this preprint due to significant concerns regarding the integrity of the work. Maintaining high standards of ethical conduct is essential to our platform. Nuclear Medicine & Medical Imaging Full Text 4 September, 2025. Research Square has withdrawn this preprint due to significant concerns regarding the integrity of the work. Maintaining high standards of ethical conduct is essential to our platform. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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