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Self-Organization Map Based Segmentation of Breast Cancer
2018
Asian Journal of Engineering and Applied Technology
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This paper presents a segmentation method by utilizing SOM. ...
Breast cancer is second major leading cause of cancer fatality in women. ...
PROPOSED FRAMEWORK FOR SEGMENTATION OF BREAST CANCER IMAGES The following fig. 1 represents proposed framework for classification of breast cancer cell using Pre-processing step, Segmentation step. ...
doi:10.51983/ajeat-2018.7.2.1015
fatcat:nfprwqkl5bafbafjauycemoqzy
IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition
2019
International Journal of Interactive Multimedia and Artificial Intelligence
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Cardoso and the Breast Research Group from the INESC Porto (Universidade do Porto) for providing the INBREAST database and for useful discussions. ...
breast cancer early. ...
rate caused by breast cancer. ...
doi:10.9781/ijimai.2018.12.006
fatcat:3rj3iqldvrclzaow4qqg37alqq
Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism-Like Optimization Algorithm
2022
BioMed Research International
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This research proposes a new multiobjective optimization technique for segmenting the breast masses from the mammographic image. ...
In recent times, breast mass is the most diagnostic sign for early detection of breast cancer, where the precise segmentation of masses is important to reduce the mortality rate. ...
[23] developed a pectoral muscle and breast boundary segmentation algorithm for early recognition of breast cancer. ...
doi:10.1155/2022/8576768
pmid:35083334
pmcid:PMC8786533
fatcat:adwuqx2xmrhqrmapf63ilzka24
Accurate Segmentation of Nuclear Regions with Multi-Organ Histopathology Images Using Artificial Intelligence for Cancer Diagnosis in Personalized Medicine
2021
Journal of Personalized Medicine
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It is considered a prerequisite for the determination of cell phenotype, nuclear morphometrics, cell classification, and the grading and prognosis of cancer. ...
Experiments were performed on two publicly available datasets: (1) The Cancer Genome Atlas (TCGA), and (2) Triple-Negative Breast Cancer (TNBC). ...
George et al. proposed an automated nuclear segmentation method [8] in breast cancer histopathology images for diagnosis and prognosis of breast cancer. ...
doi:10.3390/jpm11060515
fatcat:sphsihbahnghbf2pwgkfhsl4uq
Detection of Breast Cancer Using Histopathological Image Classification Dataset with Deep Learning Techniques
2022
BioMed Research International
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Cancer is one of the top causes of mortality, and it arises when cells in the body grow abnormally, like in the case of breast cancer. ...
In addition, a classification strategy for breast cancer detection has been developed that is based on weighted feature selection and uses an upgraded version of the Genetic Algorithm in conjunction with ...
Acknowledgments No funding is available for this research work. ...
doi:10.1155/2022/8363850
pmid:35281604
pmcid:PMC8913119
fatcat:hc3lp5tbdndedgw5wn75sfbgfe
Comparative Study on Automated Cell Nuclei Segmentation Methods for Cytology Pleural Effusion Images
2018
Journal of Healthcare Engineering
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Automated cell nuclei segmentation is the most crucial step toward the implementation of a computer-aided diagnosis system for cancer cells. ...
Therefore, this paper presents a comparative study of twelve nuclei segmentation methods for cytology pleural effusion images. ...
University, ailand, for the insightful suggestions, including their cooperation for the dataset and ground truth segmentation. ...
doi:10.1155/2018/9240389
fatcat:3lgiafvcxneehhfhckp5m4ts7y
Accurate detection of aneuploidies in array CGH and gene expression microarray data
2004
Bioinformatics
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ACKNOWLEDGEMENTS We would like to thank Peter Kasson, Mitchell Garber, Kai Li, Kara Dolinski and David Botstein for valuable discussions and help in analyzing biological results. ...
breast cancer. ...
The input-output relation for each of the filters is given on the left. y[n] is the output as a function of x[n] where n refers to gene index on the chromosome and N is the window size of each filter. ...
doi:10.1093/bioinformatics/bth440
pmid:15284100
fatcat:ugdg3d6cknffzodlcon5zjyj2y
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review
2016
IEEE Reviews in Biomedical Engineering
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In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation. ...
Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. ...
, and 58 breast cancer cell images. ...
doi:10.1109/rbme.2016.2515127
pmid:26742143
pmcid:PMC5233461
fatcat:hx5ldvsppvgzxk6rdiok7siyvi
A deep learning pipeline for breast cancer ki-67 proliferation index scoring
[article]
2022
arXiv
pre-print
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The Ki-67 proliferation index is an essential biomarker that helps pathologists to diagnose and select appropriate treatments. ...
First, semantic segmentation is performed by combining the Squeez and Excitation Resnet and Unet algorithms to extract nuclei from the background. ...
The authors would like to thank Etienne Martin, a biomedical engineer, for his valuable help. ...
arXiv:2203.07452v1
fatcat:xqvyabsajrchjlm5fh3sseyuyq
Inhomogeneous Image Segmentation using Hybrid Active Contours Model with Application to Breast Tumor Detection
2020
IEEE Access
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Thus, the proposed method offers a powerful tool for early breast cancer detection and consequent mitigation of breast cancer impacts. ...
CONCLUSION Accurate breast mass identification is critical for early breast cancer detection. ...
He is currently a Professor with the School of Computer Science and Engineering, Chung-Ang University. ...
doi:10.1109/access.2020.3029333
fatcat:pe2rkxkhdjeuxmbfwqhvby4ojq
A review of machine learning approaches, challenges and prospects for computational tumor pathology
[article]
2022
arXiv
pre-print
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in breast, colon, prostate, lung, and various tumour disease scenarios. ...
The integration of high-throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics into clinical practice improves cancer treatment plans, treatment cycles, ...
Although CNN-based U-Net has achieved good results on histological image segmentation tasks, some researchers prefer to use DL to develop algorithms for breast cancer cell nucleus segmentation [65] ...
arXiv:2206.01728v1
fatcat:g7r7fsw2bzafpkkyg6hpzjyt5e
Prediction of near-term risk of developing breast cancer using computerized features from bilateral mammograms
2014
Computerized Medical Imaging and Graphics
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A computerized breast cancer risk analysis scheme using four image processing modules, including image preprocessing, suspicious region segmentation, image feature extraction, and classification was designed ...
Asymmetry of bilateral mammographic tissue density and patterns is a potentially strong indicator of having or developing breast abnormalities or early cancers. ...
For better illustration, the segmentation example is implemented on the mammogram with malignant mass tissue (a) original image (b) enhanced image (c) segmentation result C. ...
doi:10.1016/j.compmedimag.2014.03.001
pmid:24725671
fatcat:4vojj25zfffs3etxild6v5klzy
An Effective Approach of Lesion Segmentation Within the Breast Ultrasound Image Based on the Cellular Automata Principle
2012
Journal of digital imaging
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In this paper, a novel lesion segmentation within breast ultrasound (BUS) image based on the cellular automata principle is proposed. ...
The experimental results demonstrate that the proposed method can handle BUS images with blurry boundaries and low contrast well and can segment breast lesions accurately and effectively. ...
Therefore, developing a method insensitive to initial condition and robust to noise is useful for solving a breast lesion segmentation task. ...
doi:10.1007/s10278-011-9450-6
pmid:22237810
pmcid:PMC3447089
fatcat:g77l4pfvmjczxcomvxp7d37dvy
Automatic breast ultrasound image segmentation: A survey
2018
Pattern Recognition
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In clinical routine, automatic breast ultrasound (BUS) image segmentation is very challenging and essential for cancer diagnosis and treatment planning. ...
Breast cancer is one of the leading causes of cancer death among women worldwide. ...
Cell competition: Chen et al. [138] proposed a cell-competition approach for BUS image segmentation. ...
doi:10.1016/j.patcog.2018.02.012
fatcat:mm6vwa7c3nhmvmpnmiegz6edue
Joint Inference of Clonal Structure using Single-cell DNA-Seq and RNA-Seq data
[article]
2020
bioRxiv
pre-print
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We also applied CCNMF to the paired scRNA and scDNA data from a triple-negative breast cancer xenograft, resolved its underlying clonal structures, and identified differential genes between cell clusters ...
We benchmarked CCNMF using both simulated and real cell mixture derived datasets and fully demonstrated its robustness and accuracy. ...
We applied CCNMF to characterize a gastric cancer cell line, an ovarian cancer cell mixture and a patient-derived triple negative breast cancer xenograft. ...
doi:10.1101/2020.02.04.934455
fatcat:taghxbtpavcsvdiyyjrtcb2c3a
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