☆ 4.5 Article

Cell image augmentation for classification task using GANs on Pap smear dataset

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Computer Science, Theory & Methods

Cytology Image Analysis Techniques Toward Automation: Systematically Revisited

Shyamali Mitra et al.

Summary: Cytology is a branch of pathology that focuses on diagnosing cancer and inflammatory conditions through microscopic examination of cells. Automation in cytology, specifically through image analysis techniques, has advanced significantly in various types of cytology.

ACM COMPUTING SURVEYS (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects

Zewen Li et al.

Summary: This review provides insights into the development history of CNN, a overview of various convolutions, introduction to classic and advanced CNN models, conclusions drawn from experimental analysis, rules of thumb for function and hyperparameter selection, and applications of 1-D, 2-D, and multidimensional convolutions. Moreover, it also discusses open issues and promising directions for CNN as guidelines for future work.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Cervical cell classification with graph convolutional network

Jun Shi et al.

Summary: The study introduces a novel cervical cell classification method based on Graph Convolutional Network (GCN) to explore the potential relationships of cervical cell images for improving classification performance. Experimental results demonstrate the method's superiority in cervical cell classification, particularly in handling different staining conditions.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2021)

添加到收藏夹

Review Computer Science, Artificial Intelligence

A systematic review on overfitting control in shallow and deep neural networks

Mohammad Mahdi Bejani et al.

Summary: This paper discusses the differences between shallow and deep neural networks in processing features, as well as the issue of overfitting. It provides a systematic review of overfitting control methods, categorizing them into passive, active, and semi-active subsets. Additionally, it highlights the adjustment of model complexity to data complexity, and the relationship between overfitting control, regularization, network compression, and network simplification.

ARTIFICIAL INTELLIGENCE REVIEW (2021)

添加到收藏夹

Article Medicine, General & Internal

The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19: A Geometric Transformation Perspective

Mohamed Elgendi et al.

Summary: Chest X-ray imaging technology is crucial for early detection of COVID-19, and deep learning methods can improve accuracy. Data augmentation helps reduce overfitting and enhance predictive accuracy on testing datasets.

FRONTIERS IN MEDICINE (2021)

添加到收藏夹

Article Computer Science, Information Systems

Efficient Stain-Aware Nuclei Segmentation Deep Learning Framework for Multi-Center Histopathological Images

Loay Hassan et al.

Summary: An efficient stain-aware nuclei segmentation method based on deep learning is proposed, utilizing stain clustering and an aggregation function for multi-center WSIs. Experimental results show that the method outperforms existing approaches in performance and parameter efficiency.

ELECTRONICS (2021)

添加到收藏夹

Article Biology

DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques

Md Mamunur Rahaman et al.

Summary: Cervical cancer can be prevented and treated through manual screening and deep learning techniques. The proposed hybrid deep feature fusion technology achieved good results in classifying cervical cells.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

添加到收藏夹

Review Radiology, Nuclear Medicine & Medical Imaging

A review of medical image data augmentation techniques for deep learning applications

Phillip Chlap et al.

Summary: Data augmentation has become a popular method for training deep learning models in the field of radiology and radiotherapy, where limited medical image datasets are often encountered. By generating additional training data, data augmentation can improve model performance and has been widely used in state-of-the-art deep learning models.

JOURNAL OF MEDICAL IMAGING AND RADIATION ONCOLOGY (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Diverse data augmentation for learning image segmentation with cross-modality annotations

Xu Chen et al.

Summary: The lack of annotated data is a major challenge in building reliable image segmentation models. In this paper, a diverse data augmentation generative adversarial network (DDA-GAN) is introduced to train a segmentation model for an unannotated target image domain by borrowing information from an annotated source image domain. The method effectively combines features from different domains to improve segmentation quality through data augmentation and model training.

MEDICAL IMAGE ANALYSIS (2021)

添加到收藏夹

Article Multidisciplinary Sciences

A generalized deep learning framework for whole-slide image segmentation and analysis

Mahendra Khened et al.

Summary: The study introduces a deep learning framework for histopathology tissue analysis to tackle challenges including automated segmentation, classifier training, and effective inference. The framework includes an ensemble segmentation model, WSI patch division, and efficient inference techniques. Evaluation on various histopathology tasks demonstrates the state-of-the-art performance of the proposed framework.

SCIENTIFIC REPORTS (2021)

添加到收藏夹

Article Multidisciplinary Sciences

A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

Ankur Manna et al.

Summary: This research proposes an ensemble method for cervical cancer single cell and whole slide image classification by combining three different convolutional neural network architectures. The proposed model is evaluated on two publicly available datasets, showing high classification accuracy and sensitivity.

SCIENTIFIC REPORTS (2021)

添加到收藏夹

Article Multidisciplinary Sciences

Cric searchable image database as a public platform for conventional pap smear cytology data

Mariana T. Rezende et al.

Summary: Amidst the current health crisis and social distancing measures, telemedicine has become increasingly important in healthcare, with the development of computational tools to support more efficient screening being a top priority. While the early identification of cervical cancer precursor lesions through Pap smear tests is crucial, the challenge lies in the accuracy of the traditional method and the lack of high-quality datasets for improving screening strategies. The CRIC platform's collection of cervical images has the potential to advance the automation of cytopathological analysis tasks in laboratories through machine learning algorithms.

SCIENTIFIC DATA (2021)

添加到收藏夹

Proceedings Paper Engineering, Biomedical

Hyperspectral imaging and deep learning for the detection of breast cancer cells in digitized histological images

Samuel Ortega et al.

Summary: This work demonstrates the use of hyperspectral imaging (HSI) for discriminating between normal and tumor breast cancer cells, achieving excellent results with a deep learning neural network for image classification.

MEDICAL IMAGING 2020: DIGITAL PATHOLOGY (2021)

添加到收藏夹

Proceedings Paper Engineering, Biomedical

Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology

Martin Halicek et al.

Summary: A method utilizing a generative adversarial network to synthesize hyperspectral imaging from standard RGB images of breast cancer tissue has been proposed in this study. The synthesized hyperspectral images achieved high structural similarity and low mean absolute error compared to actual hyperspectral images. Further research is needed to validate the effectiveness of this method on larger datasets.

MEDICAL IMAGING 2020: DIGITAL PATHOLOGY (2021)

添加到收藏夹

Article Oncology

Cervical Cancer Screening: Comparison of Conventional Pap Smear Test, Liquid-Based Cytology, and Human Papillomavirus Testing as Stand-alone or Cotesting Strategies

Linda A. Liang et al.

Summary: The study compared the strategies of stand-alone HPV testing and cotesting for cervical cancer screening in a population-based study in Germany. The results showed that cotesting had higher sensitivity but also resulted in more false positive results and colposcopy referrals compared to stand-alone HPV testing.

CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION (2021)

添加到收藏夹

Review Computer Science, Artificial Intelligence

Segmentation of cervical cells for automated screening of cervical cancer: a review

Abid Sarwar et al.

ARTIFICIAL INTELLIGENCE REVIEW (2020)

添加到收藏夹

Article Engineering, Biomedical

An image augmentation approach using two-stage generative adversarial network for nuclei image segmentation

Siddharth Pandey et al.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network

Vivek Kumar Singh et al.

EXPERT SYSTEMS WITH APPLICATIONS (2020)

添加到收藏夹

Article Chemistry, Multidisciplinary

Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap Smear Images

Kyi Pyar Win et al.

APPLIED SCIENCES-BASEL (2020)

添加到收藏夹

Article Multidisciplinary Sciences

Test-time augmentation for deep learning-based cell segmentation on microscopy images

Nikita Moshkov et al.

SCIENTIFIC REPORTS (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Improving classification accuracy using data augmentation on small data sets

Francisco J. Moreno-Barea et al.

EXPERT SYSTEMS WITH APPLICATIONS (2020)

添加到收藏夹

Article Medicine, General & Internal

Comparison of Liquid-Based Cytology and Conventional Papanicolaou Smear for Cervical Cancer Screening: An Experience From Pakistan

Atif A. Hashmi et al.

CUREUS JOURNAL OF MEDICAL SCIENCE (2020)

添加到收藏夹

Review Oncology

A Narrative Review: Classification of Pap Smear Cell Image for Cervical Cancer Diagnosis

Wan Azani Mustafa et al.

ONCOLOGIE (2020)

添加到收藏夹

Article Computer Science, Information Systems

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

Md Mamunur Rahaman et al.

IEEE ACCESS (2020)

添加到收藏夹

Article Engineering, Biomedical

A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images

Wasswa William et al.

BIOMEDICAL ENGINEERING ONLINE (2019)

添加到收藏夹

Article Computer Science, Theory & Methods

A survey on Image Data Augmentation for Deep Learning

Connor Shorten et al.

JOURNAL OF BIG DATA (2019)

添加到收藏夹

Article Obstetrics & Gynecology

Can LBC Completely Replace Conventional Pap Smear in Developing Countries

Vasundhara Kamineni et al.

JOURNAL OF OBSTETRICS AND GYNECOLOGY OF INDIA (2019)

添加到收藏夹

Article Computer Science, Information Systems

Segmentation and Classification of Cervical Cells Using Deep Learning

Kurnianingsih et al.

IEEE ACCESS (2019)

添加到收藏夹

Article Computer Science, Information Systems

Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection

Changhee Han et al.

IEEE ACCESS (2019)

添加到收藏夹

Article Oncology

Impact of Widespread Cervical Cancer Screening: Number of Cancers Prevented and Changes in Race-specific Incidence

Daniel X. Yang et al.

AMERICAN JOURNAL OF CLINICAL ONCOLOGY-CANCER CLINICAL TRIALS (2018)

添加到收藏夹

Review Computer Science, Interdisciplinary Applications

A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images

Wasswa William et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2018)

添加到收藏夹

Article Immunology

Comparative study between liquid-based cytology & conventional Pap smear for cytological follow up of treated patients of cancer cervix

Uma Singh et al.

INDIAN JOURNAL OF MEDICAL RESEARCH (2018)

添加到收藏夹

Article Computer Science, Artificial Intelligence

GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification

Maayan Frid-Adar et al.

NEUROCOMPUTING (2018)

添加到收藏夹

Article Computer Science, Information Systems

DeepPap: Deep Convolutional Networks for Cervical Cell Classification

Ling Zhang et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2017)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Automated classification of Pap smear images to detect cervical dysplasia

Kangkana Bora et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2017)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Densely Connected Convolutional Networks

Gao Huang et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

添加到收藏夹

Proceedings Paper Computer Science, Theory & Methods

Performance Analysis of GPU-based Convolutional Neural Networks

Xiaqing Li et al.

PROCEEDINGS 45TH INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING - ICPP 2016 (2016)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Unsupervised segmentation of cervical cell images using Gaussian Mixture Model

Srikanth Ragothaman et al.

PROCEEDINGS OF 29TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, (CVPRW 2016) (2016)

添加到收藏夹

Review Cell Biology

Bethesda 2014: improving on a paradigm shift

D. C. Wilbur et al.

CYTOPATHOLOGY (2015)

添加到收藏夹

Article Computer Science, Interdisciplinary Applications

Automatic cervical cell segmentation and classification in Pap smears

Thanatip Chankong et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2014)

添加到收藏夹

Article Pathology

Pap smear cell image classification using global MPEG-7 descriptors

Luz H Camargo et al.

Diagnostic Pathology (2013)

添加到收藏夹

Article Public, Environmental & Occupational Health

Reduced cervical cancer incidence and mortality in Canada: national data from 1932 to 2006

James A. Dickinson et al.

BMC PUBLIC HEALTH (2012)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Unsupervised segmentation and classification of cervical cell images

Asli Genctav et al.

PATTERN RECOGNITION (2012)

添加到收藏夹

Review Oncology

An overview of prevention and early detection of cervical cancers

Gauravi A. Mishra et al.

INDIAN JOURNAL OF MEDICAL AND PAEDIATRIC ONCOLOGY (2011)

添加到收藏夹

Article Biology

Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification

Yannis Marinakis et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2009)

添加到收藏夹

Article Immunology

Public health policy for cervical cancer prevention: The role of decision science, economic evaluation, and mathematical modeling

Sue J. Goldie et al.

VACCINE (2006)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.