Detection of cervical cell based on multi-scale spatial information
Abstract Cervical cancer poses a significant health risk to women. Deep learning methods can assist pathologists in quickly screening images of suspected lesion cells, greatly improving the efficiency of cervical cancer screening and diagnosis. However, existing deep learning methods rely solely on...
Saved in:
Main Authors: | Gang Li, Xinyu Fan, Chuanyun Xu, Pengfei Lv, Ru Wang, Zihan Ruan, Zheng Zhou, Yang Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-01-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-025-87165-7 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion
by: Yiming Zhang, et al.
Published: (2025-01-01) -
Anomaly Usage Behavior Detection Based on Multi-Source Water and Electricity Consumption Information
by: Wenqing Zhou, et al.
Published: (2025-01-01) -
Multi-Scale Feature Fusion and Context-Enhanced Spatial Sparse Convolution Single-Shot Detector for Unmanned Aerial Vehicle Image Object Detection
by: Guimei Qi, et al.
Published: (2025-01-01) -
Spatial integration of multi-omics single-cell data with SIMO
by: Penghui Yang, et al.
Published: (2025-02-01) -
Data Temperature Informed Streaming for Optimising Large-Scale Multi-Tiered Storage
by: Dominic Davies-Tagg, et al.
Published: (2024-06-01)