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Engineering and Applied Science
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Cincinnati, Ohio
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- Type:
- Article
- 摘抄:
- 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.
- 作者:
- Debsarkar, Shyam
- 提交者:
- Shyam Debsarkar
- 上传日期:
- 12/28/2025
- 更改日期:
- 12/28/2025
- 创建:
- March 25, 2025
- 证书:
- All rights reserved
-
- Type:
- Document
- 摘抄:
- This dataset details the force-displacement response of porcine meniscus under tensile-fracture behavior. Samples are cut from the anterior, middle and posterior regions of the meniscus. Each specimen geometry dimension is included.
- 作者:
- Chia-Ying Lin; Kumar Vemaganti, and Long, Teng
- 提交者:
- Teng Long
- 上传日期:
- 03/03/2023
- 更改日期:
- 03/03/2023
- 创建:
- 2020-11
- 证书:
- Attribution 4.0 International
-
- Type:
- Dataset
- 摘抄:
- The Dataset contains raw data that indicates the start and stop time of water flowing at fixtures in the Marian Spencer Hall Cafeteria restroom during hours of operation. The data were collected as part of an effort to develop and test a novel method of measuring flow to calculate the probability that the fixture is busy (fixture p-value). The fixture p-value is one of the parameters necessary to predict peak demand in buildings for pipe sizing purposes. There are two .csv files, a README file and a sample of the data collection template with contact information. The dataset also contains a MATLAB code written to accept data in the suggested format and estimate the fixture probability of use.
- 作者:
- Choudhary, Chandrashekhar ; Omaghomi, Toju; Buchberger, Steven; Wang, Tianshuo, and Tao, Li
- 提交者:
- Toju Omaghomi
- 上传日期:
- 12/19/2022
- 更改日期:
- 12/19/2022
- 创建:
- 2022-12
- 证书:
- Open Data Commons Open Database License (ODbL)
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- Type:
- Dataset
- 摘抄:
- This dataset details the force-displacement response of porcine meniscus in no-slip uniaxial compression. Samples are cut from the anterior, middle and posterior regions of the meniscus.
- 作者:
- Vemaganti, Kumar ; Lin, Chia-Ying, and Long, Teng
- 提交者:
- Kumar Vemaganti
- 上传日期:
- 02/24/2022
- 更改日期:
- 02/24/2022
- 创建:
- 2020
- 证书:
- Open Data Commons Open Database License (ODbL)