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Rethinking the Semantics of "Cutaneous Squamous Cell Carcinoma In Situ".
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Department of Dermatology, Yale School of Medicine, New Haven, Connecticut, USA.
The summarization capabilities of pretrained and large language models (LLMs) have been widely validated in general areas, but their use in scientific corpus, which involves complex sentences and specialized knowledge, has been less assessed. This paper presents conceptual and experimental analyses of scientific summarization, highlighting the inadequacies of traditional evaluation methods, such as -gram, embedding comparison, and QA, particularly in providing explanations, grasping scientific concepts, or identifying key content. Subsequently, we introduce the Facet-aware Metric (FM), employing LLMs for advanced semantic matching to evaluate summaries based on different aspects.
View Article and Find Full Text PDFUSCT-UNet: Rethinking the Semantic Gap in U-Net Network From U-Shaped Skip Connections With Multichannel Fusion Transformer.
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Medical image segmentation is a crucial component of computer-aided clinical diagnosis, with state-of-the-art models often being variants of U-Net. Despite their success, these models' skip connections introduce an unnecessary semantic gap between the encoder and decoder, which hinders their ability to achieve the high precision required for clinical applications. Awareness of this semantic gap and its detrimental influences have increased over time.
View Article and Find Full Text PDFRethinking masked image modelling for medical image representation.
Med Image Anal
December 2024
University of Adelaide, Australia. Electronic address:
Masked Image Modelling (MIM), a form of self-supervised learning, has garnered significant success in computer vision by improving image representations using unannotated data. Traditional MIMs typically employ a strategy of random sampling across the image. However, this random masking technique may not be ideally suited for medical imaging, which possesses distinct characteristics divergent from natural images.
View Article and Find Full Text PDFNon-visual spatial strategies are effective for maintaining precise information in visual working memory.
Cognition
October 2024
Otto von Guericke University, Medical Faculty, Magdeburg, Germany; Leibniz Institute for Neurobiology, Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Germany; Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Jena-Magdeburg, Halle, Germany.
Visual working memory content is commonly thought to be composed of a precise visual representation of stimulus information (e.g., color, shape).
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