This dataset contains an original article, "Maya Pottery Red: Hue as a Perceptual Prior for Object Detection in Remote Sensing for Drone-based Areal Survey of Maya Settlements" and supplementary material for the article, including results and source data.
As I have previously shown, Alexandre Brongniart established a coherent science of ceramics. By the mid-nineteenth century, Brongniart had popularised the term "la céramique" as a widely-applicable name for the field of pottery and porcelain making, and other related arts. In the Twentieth Century, ceramic manufacturing became increasingly technical. The inclusive field of artisans and industrialists that Brongniart had once envisioned was fracturing. Voices called for the separation of pottery making from experimental, industrial ceramics and the meaning of the term “ceramics” was hotly debated. Numerous etymologies were traced, but, as the predominant language of science transferred from French to English, none of the twentieth-century authors recognized Brongniart’s key role in the invention of the term. Critically, this language debate coincided with and reflected the global politics, nationalism, and warfare of the first half of the Twentieth Century.
Taking on the task of ordering the sciences related to pottery and clay-based objects, natural historian and porcelainier Alexandre Brongniart sought a new way of describing the ancient practice. Early in his forty-seven-year career as director of the Sèvres Porcelain Manufactory, Brongniart developed a research center for the advanced study of pottery and porcelain making. Brongniart recognized that an inclusive and distinct term for the field was necessary, but it had to be introduced carefully, so that it was welcomed rather than rejected as presumptuous. Through close reading of Brongniart’s writings, as well as contemporary periodicals and the texts of other authors, the development of the word “ceramic” – originally introduced by Brongniart and his associates in French as “la céramique” – can be traced closely. I show that this was a deliberate, methodical, and years-long effort to create a durable, comprehensive term.
Histopathology image analysis plays a pivotal role in disease diagnosis and treatment planning, relying heavily on accurate nuclei segmentation for extracting vital cellular information. In recent years, artificial intelligence (AI) and in particular deep learning models have been applied successfully in solving computational pathology image analysis tasks. The You Only Look Once (YOLO) object detection framework, which is based on a convolutional neural network (CNN) architecture has gained traction across various domains for its real-time processing capabilities. This systematic review aims to comprehensively explore and evaluate the advancements, challenges, and applications of YOLO-based methodologies in nuclei segmentation within the domain of histopathological images. The review encompasses a structured analysis of recent literature, focusing on the utilization of YOLO variants for nuclei segmentation. Key methodologies, training strategies, dataset specifics, and performance metrics are evaluated to elucidate the strengths and limitations of YOLO in this context. Additionally, the review highlights the unique characteristics of YOLO that enable efficient object detection and delineation of nuclei structures, offering a comparative analysis against traditional segmentation approaches. This systematic review underscores the promising outcomes achieved through YOLO-based architectures, emphasizing their potential for accurate and rapid nuclei segmentation. Furthermore, it identifies persistent challenges such as handling variances in nuclei appearances, optimizing model architectures for histopathological images, and improving generalization across diverse datasets. Insights derived from this review can provide a foundation for future research directions and enhancements in nuclei segmentation methodologies using YOLO within histopathology, fostering advancements in disease diagnosis and biomedical research.
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