Related references
Note: Only part of the references are listed.Outcomes of High-Grade Cervical Dysplasia with Positive Margins and HPV Persistence after Cervical Conization
Andrea Giannini et al.
Vaccines (2023)
NCA-based hybrid convolutional neural network model for classification of cervical cancer on gauss-enhanced pap-smear images
Harun Bingol
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY (2022)
Detection and classification of cervical cancer images using CEENET deep learning approach
T. G. Subarna et al.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS (2022)
Cervical cancer classification using efficient net and fuzzy extreme learning machine
A. Suphalakshmi et al.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS (2022)
Automatic identifying and counting blood cells in smear images by using single shot detector and Taguchi method
Yao-Mei Chen et al.
BMC BIOINFORMATICS (2022)
Recent trends in smartphone-based detection for biomedical applications: a review
Soumyabrata Banik et al.
ANALYTICAL AND BIOANALYTICAL CHEMISTRY (2021)
Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
Hyuna Sung et al.
CA-A CANCER JOURNAL FOR CLINICIANS (2021)
Cervical cancer detection in pap smear whole slide images using convNet with transfer learning and progressive resizing
Anant R. Bhatt et al.
PEERJ COMPUTER SCIENCE (2021)
Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears
Xiaohui Zhu et al.
NATURE COMMUNICATIONS (2021)
Cric searchable image database as a public platform for conventional pap smear cytology data
Mariana T. Rezende et al.
SCIENTIFIC DATA (2021)
Towards the Mobile Detection of Cervical Lesions: A Region-Based Approach for the Analysis of Microscopic Images
Ana Filipa Sampaio et al.
IEEE ACCESS (2021)
A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification
Debora N. Diniz et al.
JOURNAL OF IMAGING (2021)
Cervical Cancer Cell Detection Based on Deep Convolutional Neural Network
Mingyang Xia et al.
PROCEEDINGS OF THE 39TH CHINESE CONTROL CONFERENCE (2020)
Automated screening of sickle cells using a smartphone-based microscope and deep learning
Kevin de Haan et al.
NPJ DIGITAL MEDICINE (2020)
A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification
Teresa Conceicao et al.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2019)
U-Net: deep learning for cell counting, detection, and morphometry
Thorsten Falk et al.
NATURE METHODS (2019)
Selective Detection and Segmentation of Cervical Cells
Jing Ke et al.
ICBBT 2019: 2019 11TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL TECHNOLOGY (2019)
Cervical cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up (vol 28, pg 72, 2017)
C. Marth et al.
ANNALS OF ONCOLOGY (2018)
Microscopy cell counting and detection with fully convolutional regression networks
Weidi Xie et al.
COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION (2018)
Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination
Luis Rosado et al.
SENSORS (2017)
Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium
Oscar Holmstrom et al.
GLOBAL HEALTH ACTION (2017)
Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells
Zhi Lu et al.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2017)
An Improved Joint Optimization of Multiple Level Set Functions for the Segmentation of Overlapping Cervical Cells
Zhi Lu et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2015)
Fluorescent Imaging of Single Nanoparticles and Viruses on a Smart Phone
Qingshan Wei et al.
ACS NANO (2013)