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MedCLIP-SAMv2: Towards universal text-driven medical image segmentation

Author(s): Koleilat T; Asgariandehkordi H; Rivaz H; Xiao Y;

Segmentation of anatomical structures and pathologies in medical images is essential for modern disease diagnosis, clinical research, and treatment planning. While significant advancements have been made in deep learning-based segmentation techniques, many of these methods still suffer from limitations in data efficiency, generalizability, and interactivi ...

Article GUID: 40779830


Statistical or Embodied? Comparing Colorseeing, Colorblind, Painters, and Large Language Models in Their Processing of Color Metaphors

Author(s): Nadler EO; Guilbeault D; Ringold SM; Williamson TR; Bellemare-Pepin A; Com?a IM; Jerbi K; Narayanan S; Aziz-Zadeh L;

Can metaphorical reasoning involving embodied experience-such as color perception-be learned from the statistics of language alone? Recent work finds that colorblind individuals robustly understand and reason abstractly about color, implying that color associations in everyday language might cont ...

Article GUID: 40621800


Large language models deconstruct the clinical intuition behind diagnosing autism

Author(s): Stanley J; Rabot E; Reddy S; Belilovsky E; Mottron L; Bzdok D;

Efforts to use genome-wide assays or brain scans to diagnose autism have seen diminishing returns. Yet the clinical intuition of healthcare professionals, based on longstanding first-hand experience, remains the gold standard for diagnosis of autism. We leveraged deep learning to deconstruct and interrogate the logic of expert clinician intuition from cli ...

Article GUID: 40147442


Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism

Author(s): Cheligeer C; Southern DA; Yan J; Wu G; Pan J; Lee S; Martin EA; Jafarpour H; Eastwood CA; Zeng Y; Quan H;

Objectives: Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinical documentation, and the complexity of data formats. Pulmonary embolism as an adverse event (PEAE) is particularly difficult to identify using exi ...

Article GUID: 40105654


Ion channel classification through machine learning and protein language model embeddings

Author(s): Ghazikhani H; Butler G;

Ion channels are critical membrane proteins that regulate ion flux across cellular membranes, influencing numerous biological functions. The resource-intensive nature of traditional wet lab experiments for ion channel identification has led to an increasing emphasis on computational techniques. This study extends our previous work on protein language mode ...

Article GUID: 39572876


Exploiting protein language models for the precise classification of ion channels and ion transporters

Author(s): Ghazikhani H; Butler G;

This study introduces TooT-PLM-ionCT, a comprehensive framework that consolidates three distinct systems, each meticulously tailored for one of the following tasks: distinguishing ion channels (ICs) from membrane proteins (MPs), segregating ion transporters (ITs) from MPs, and differentiating ICs from ITs. Drawing upon the strengths of six Protein Languag ...

Article GUID: 38656743


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