2025年1月7日 · Through this work, we seek to provide a large dataset of cervical cytology images with exhaustive annotations of abnormal cervical cells. The dataset consists of 8,037 images derived from 129...

cervical cytology images with exhaustive annotations of abnormal cervical cells. the dataset consists of 8,037 images derived from 129 scanned Thinprep cytologic test (TCT) slide images.

2024年5月22日 · Here, the authors use artificial intelligence to predict cytology grades from whole slide images. Nature Communications - Cervical screening is a key method for detecting cervical cancer, but is...

2021年6月9日 · TCT检查就能够准确敏感的发现早期宫颈癌前病变。如果检查出来宫颈糜烂，大家需要检查TCT，那么正常tct报告单图片是怎样的呢？结果我们怎么看？ TCT主要检查是否hpv感染. 1正常tct报告单图片——有炎症反应

2023年10月1日 · Automated detection of cervical abnormal cells from Thin-prep cytologic test (TCT) images is essential for efficient cervical abnormal screening by computer-aided diagnosis system.

An AI algorithm has been developed to detect cancerous cervical cells on TCT images acquired by slide scanner to help improve pathologists' efficiency.

2021年1月7日 · In our study, a CNN-based TCT cervical-cancer screening model was established through a retrospective study of multicenter TCT images. This model shows improved speed and accuracy for cervical cancer screening, and helps overcome the shortage of medical resources required for cervical cancer screeni …

After expanding the TCT data set, experiments show that the improved model in this paper can effectively improve the accuracy, recall and segmentation accuracy of epithelial cells, white blood cells, and fungi.

2021年9月30日 · Find tct stock images in HD and millions of other royalty-free stock photos, illustrations and vectors in the Shutterstock collection. Thousands of new, high-quality pictures added every day.

2023年3月9日 · In this work, we proposed a robust DCNN for cervical cancer screening using whole-slide images (WSI) of ThinPrep cytologic test (TCT) slides from 211 cervical cancer and 189 normal patients. We used an active learning strategy to improve the efficiency and accuracy of image labeling.