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Abstract Cytopathology is central to cancer screening and diagnosis but remains labor-intensive, motivating the development of scalable computational solutions. Here, we present CROWN (Cytology visual foundation netwoRk Optimized With self-supervised learNing), a universal visual foundation model for computational cytopathology.
Abstract Intraoperative pathology is pivotal to precision surgery, yet its clinical impact is constrained by diagnostic complexity and the limited availability of high-quality frozen-section data. While computational pathology has made significant strides, the lack of large-scale, prospective validation has impeded its routine adoption in surgical workflows.
Abstract The precision assembly of anisotropic nanostructures is a prerequisite for next-generation integrated microsystems, quantum photonics, and bio-interfaces. However, bridging the gap between individual manipulation accuracy and scalable, programmable manufacturing remains a grand challenge. Current optical and alternative physical field approaches are hindered by inadequate control stability, thermal damage, and interfacial adhesion limitations.
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