breast cancer dataset uci

[View Context].Yuh-Jeng Lee. Dept. Heisey, and O.L. Olvi L. Mangasarian, Computer Sciences Dept., University of Wisconsin 1210 West Dayton St., Madison, WI 53706 olvi '@' cs.wisc.edu Donor: Nick Street, Each record represents follow-up data for one breast cancer case. Street, W.H. [View Context].Andrew I. Schein and Lyle H. Ungar. Boosted Dyadic Kernel Discriminants. ICANN. An evolutionary artificial neural networks approach for breast cancer … Res. Tags: brca1, breast, breast cancer, cancer, carcinoma, ovarian cancer, ovarian carcinoma, protein, surface View Dataset Chromatin immunoprecipitation profiling of human breast cancer cell lines and tissues to identify novel estrogen receptor-{alpha} binding sites and estradiol target genes Thanks go to M. Zwitter and M. Soklic for providing the data. The original Wisconsin-Breast Cancer (Diagnostics) dataset (WBC) from UCI machine learning repository is a classification dataset, which records the measurements for breast cancer cases. Wolberg, W.N. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes. Sys. For a … Neurocomputing, 17. Data. Department of Computer Science University of Massachusetts. [Web Link] O.L. breast cancer and no evidence of distant metastases at the time of diagnosis. Gavin Brown. 2002. A Parametric Optimization Method for Machine Learning. Goal: To create a classification model that looks at predicts if the cancer diagnosis … Diversity in Neural Network Ensembles. 1996. of Decision Sciences and Eng. The most effective way to reduce numbers of death is early detection. 2004. School of Computing National University of Singapore. Neural Networks Research Centre Helsinki University of Technology. Fig 1. These are consecutive patients seen by Dr. Wolberg since 1984, and include only those cases exhibiting invasive breast cancer … Introduction. KDD. of Mathematical Sciences One Microsoft Way Dept. Wolberg. The Recurrence Surface Approximation (RSA) method is a linear programming model which predicts Time To Recur using both recurrent and nonrecurrent cases. 17 No. The University of Birmingham. Efficient Discovery of Functional and Approximate Dependencies Using Partitions. Create a classifier that can predict the risk of having breast cancer with routine parameters for early detection. 17, pages 257-264, 1995. Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. "-//W3C//DTD HTML 4.01 Transitional//EN\">, Breast Cancer Coimbra Data Set We currently maintain 559 data sets as a service to the machine learning community. Street, D.M. Applied Economic Sciences. It gives information on tumor features such as tumor size, density, and texture. NeuroLinear: From neural networks to oblique decision rules. Please submit: (1) your source code that i should be able to (compile and) run, and the processed dataset if any; (2) a report on a program checklist, how you accomplish the project, and the result of your classification. Papers That Cite This Data Set 1: Gavin Brown. Microsoft Research Dept. To create the classification of breast cancer stages and to train the model using the KNN algorithm for predict breast cancers, as the initial step we need to find a dataset. of Engineering Mathematics. Statistical methods for construction of neural networks. The details are described in [Patricio, 2018] - [Web Link]. [Web Link] W.H. Unsupervised and supervised data classification via nonsmooth and global optimization. 2001. [View Context].Kristin P. Bennett and Ayhan Demiriz and Richard Maclin. [View Context].Justin Bradley and Kristin P. Bennett and Bennett A. Demiriz. Wolberg. [View Context].Charles Campbell and Nello Cristianini. Approximate Distance Classification. [View Context].P. Abstract: Clinical features were observed or measured for 64 patients with breast cancer and 52 healthy controls. Preliminary Thesis Proposal Computer Sciences Department University of Wisconsin. CEFET-PR, Curitiba. Wolberg, W.N. LIBSVM Data: Classification, Regression, and Multi-label. 3.2 Breast Cancer Dataset The feature form this dataset are computed from a digitized image of a fine needle aspirate (FNA) of a breast tumor. [View Context].Adil M. Bagirov and Alex Rubinov and A. N. Soukhojak and John Yearwood. A Family of Efficient Rule Generators. [View Context].Baback Moghaddam and Gregory Shakhnarovich. An inductive learning approach to prognostic prediction. Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. Exploiting unlabeled data in ensemble methods. Artificial Intelligence in Medicine, 25. They describe characteristics of the cell nuclei present in the image. Wolberg, W.N. The malignant class of this dataset is downsampled to 21 points, which are considered as outliers, while points in the benign class are considered inliers. Tags: cancer, colon, colon cancer View Dataset A phase II study of adding the multikinase sorafenib to existing endocrine therapy in patients with metastatic ER-positive breast cancer. admissions: Gender bias among graduate school admissions to UC Berkeley. There are 9 input variables all of which a nominal. [View Context]. [View Context].Rudy Setiono. This breast cancer domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. of Decision Sciences and Eng. Solution Introduction. 1998. Quantitative Attributes: Age (years) BMI (kg/m2) Glucose (mg/dL) Insulin (µU/mL) HOMA Leptin (ng/mL) Adiponectin (µg/mL) Resistin (ng/mL) MCP-1(pg/dL) Labels: 1=Healthy controls 2=Patients, This dataset is publicly available for research. The Breast Cancer Dataset: ... perimeter, area, texture, smoothness, compactness, concavity, symmetry etc). Inspiration. The first 30 features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. This dataset is taken from UCI machine learning repository. Characterization of the Wisconsin Breast cancer Database Using a Hybrid Symbolic-Connectionist System. pl. A few of the images … The performance of the study is measured with respect to accuracy, sensitivity, specificity, precision, negative predictive value, false-negative rate, false-positive rate, F1 score, and Matthews Correlation Coefficient. 1996. Smooth Support Vector Machines. Department of Information Systems and Computer Science National University of Singapore. Mangasarian. S and Bradley K. P and Bennett A. Demiriz. Department of Information Systems and Computer Science National University of Singapore. The distribution of benign cancer cells is more uniform and structural malignancies are found in malignant cancer cells as shown in these figures. Constrained K-Means Clustering. Department of Computer Methods, Nicholas Copernicus University. Feature Minimization within Decision Trees. W. Nick Street, Computer Sciences Dept. I opened it with Libre Office Calc add the column names as described on the breast-cancer-wisconsin NAMES file, and save the file as csv. Data Set Information: Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. brca: Breast Cancer Wisconsin Diagnostic Dataset from UCI Machine... brexit_polls: Brexit Poll Data death_prob: 2015 US Period Life Table divorce_margarine: Divorce rate and margarine consumption data ds_theme_set: dslabs theme set gapminder: Gapminder Data greenhouse_gases: Greenhouse gas concentrations over 2000 … After importing useful libraries I have imported Breast Cancer dataset, then first step is to separate features and labels from dataset then we will encode the categorical data, after that we have split entire dataset into … BreastCancer Wisconsin Diagnostic dataset. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets. It is a dataset of Breast Cancer patients with Malignant and Benign tumor. [View Context].Chotirat Ann and Dimitrios Gunopulos. svm sklearn pandas breast-cancer-wisconsin Updated Jun 10, 2019; Jupyter Notebook; pranath / breast_cancer_prediction Star 0 Code Issues Pull requests In this project I will look at a dataset of patient data relating to breast cancer… Breast Cancer Services Whether you have a family history of breast cancer, a suspicious lump or pain, or need regular screening, our breast cancer specialists at the UCI Health Chao Family Comprehensive Cancer Center can ease your worries with state-of-the-art care.. Our experienced team at Orange County's only National Institute of Cancer-designated comprehensive cancer … [View Context]. Knowl. If you publish results when using this database, then please include this information in your acknowledgements. Irvine, Calif., Oct. 7, 2020 – Electrical engineers, computer scientists and biomedical engineers at the University of California, Irvine have created a new lab-on-a-chip that can help study tumor heterogeneity to reduce resistance to cancer therapies.. Figures 1 and 2 show examples of benign and malignant cancer cells in the dataset. The video has sound issues. We are applying Machine Learning on Cancer Dataset for Screening, prognosis/prediction, especially for Breast Cancer. Breast cancer diagnosis and prognosis via linear programming. Heisey, and O.L. The ANNIGMA-Wrapper Approach to Neural Nets Feature Selection for Knowledge Discovery and Data Mining. Once you have had a look through this why not try changing the load data line to the iris data set we have seen before and see how the same code works there (where there are three possible outcomes). Heterogeneous Forests of Decision Trees. https://goo.gl/U2Uwz2. 2000. Department of Computer and Information Science Levine Hall. Operations Research, 43(4), pages 570-577, July-August 1995. 2004. First, I downloaded UCI Machine Learning Repository for breast cancer dataset. A. K Suykens and Guido Dedene and Bart De Moor and Jan Vanthienen and Katholieke Universiteit Leuven. An evolutionary artificial neural networks approach for breast cancer diagnosis. University of Wisconsin, Clinical Sciences Center Madison, WI 53792 wolberg '@' eagle.surgery.wisc.edu 2. Read more in the User Guide. 1998. Mangasarian. please bare with us.This video will help in demonstrating the step-by-step approach to download Datasets from the UCI repository. 2002. Breast cancer is the most common cancer occurring among women, and this is also the main reason for dying from cancer in the world. [View Context].Lorne Mason and Peter L. Bartlett and Jonathan Baxter. Summary This is an analysis of the Breast Cancer Wisconsin (Diagnostic) DataSet, obtained from Kaggle We are going to analyze it and to try several machine learning classification models to compare their … Breast Cancer Wisconsin (Diagnostic) Data Set Predict whether the cancer is benign or malignant. They describe characteristics of the cell nuclei present in the image. 1998. Broad Institute Cancer Programs Datasets; Medicare Data; Mental Health in Tech; UCI Student Alcohol Consumption Dataset; NIH Chest X-Ray Dataset; California Kindergarten Vaccinations; Classifying Breast Cancer … UCI-Data-Analysis / Breast Cancer Dataset / breastcancer.py / Jump to. Predicting Breast Cancer (Wisconsin Data Set) using R ; by Raul Eulogio; Last updated almost 3 years ago Hide Comments (–) Share Hide Toolbars Analytical and Quantitative Cytology and Histology, Vol. In this work, the Wisconsin Breast Cancer dataset was obtained from the UCI Machine Learning Repository. The full details about the Breast Cancer Wisconin data set can be found here - [Breast Cancer Wisconin Dataset][1]. Also, please cite … IEEE Trans. This is a complete report about this dataset from UCI datasets. Data Set Information: There are 10 predictors, all quantitative, and a binary dependent variable, indicating the presence or absence of breast cancer. 2000. Sys. 2000. [View Context].Hussein A. Abbass. Just replace the first line of the # Load dataset section with: data_set = datasets.load_breast_cancer() I download the file from the Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+ (Original)) The file was in.data format. 1996. [Web Link] See also: [Web Link] [Web Link]. Many are from UCI, Statlog, StatLib and other collections. n the 3-dimensional space is that … There are 10 predictors, all quantitative, and a binary dependent variable, indicating the presence or absence of breast cancer. You may view all data sets through our searchable interface. Image analysis and machine learning applied to breast cancer diagnosis and prognosis. UCI Machine Learning Repository. You can learn more about the datasets in the UCI Machine Learning Repository. The predictors are anthropometric data and parameters … Microsoft Research Dept. Computer-derived nuclear ``grade'' and breast cancer prognosis. Street, and O.L. 1995. [View Context].Wl odzisl/aw Duch and Rudy Setiono and Jacek M. Zurada. W.H. [View Context].Geoffrey I. Webb. Hussein A. Abbass. They describe characteristics of the cell nuclei present in the image. [View Context].Krzysztof Grabczewski and Wl/odzisl/aw Duch. [View Context].András Antos and Balázs Kégl and Tamás Linder and Gábor Lugosi. Machine Learning, 38. [View Context].Rudy Setiono and Huan Liu. Predicting Breast Cancer (Wisconsin Data Set) using R ; by Raul Eulogio; Last updated almost 3 years ago Hide Comments (–) Share Hide Toolbars To create the dataset Dr. Wolberg used fluid samples, taken from patients with solid breast masses and an easy-to-use graphical computer program called Xcyt, which is capable of … A Monotonic Measure for Optimal Feature Selection. The breast cancer database is a publicly available dataset from the UCI Machine learning Repository. [Web Link]. Neural-Network Feature Selector. ICML. UCI Machine Learning Repository. 2, pages 77-87, April 1995. Please refer to the Machine Learning Data Eng, 12. Data-dependent margin-based generalization bounds for classification. (Benign) of the 569 breast cancer data in the dataset. This dataset is taken from OpenML - breast-cancer. [View Context].Erin J. Bredensteiner and Kristin P. Bennett. Please include this citation if you plan to use this database: [Patricio, 2018] Patrício, M., Pereira, J., Crisóstomo, J., Matafome, P., Gomes, M., Seiça, R., & Caramelo, F. (2018). Journal of Machine Learning Research, 3. Computerized breast cancer diagnosis and prognosis from fine needle aspirates. Street and W.H. Using Resistin, glucose, age and BMI to predict the presence of breast cancer. Simple Learning Algorithms for Training Support Vector Machines. Nick Street. Breast cancer predictions using UCI's Breast cancer Wisconsin dataset. This is the same dataset used by Bennett [ 23 ] to detect cancerous and noncancerous tumors. UCI Machine Learning • updated 4 years ago (Version 2) Data Tasks (2) Notebooks (1,493) Discussion (34) … 2002. Created on Sat Jan 02 13:54:19 2016: Analysis of the wisconsin breast cancer dataset: @author: Rupak Chakraborty """ import numpy as np: import pandas as pd: from sklearn. Morgan Kaufmann. In A. Prieditis and S. Russell, editors, Proceedings of the Twelfth International Conference on Machine Learning, pages 522--530, San Francisco, 1995. [View Context].Jarkko Salojarvi and Samuel Kaski and Janne Sinkkonen. Sete de Setembro, 3165. This breast cancer databases was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. 1997. Computational intelligence methods for rule-based data understanding. 2000. A hybrid method for extraction of logical rules from data. Proceedings of ANNIE. 1998. 10 . Intell. of Mathematical Sciences One Microsoft Way Dept. A-Optimality for Active Learning of Logistic Regression Classifiers. Benign cancer cell samples [18, 19] Asuncion, 2007 #3, #4 Constrained K-Means Clustering. Women is diagnosed somewhere in the space of 1-4 features and 1-3 separating planes Note that Original! And Eddy Mayoraz and Ilya B. Muchnik Suykens and Guido Dedene and Bart De Moor and Jan Vanthienen Katholieke. And Bennett A. Demiriz Wolberg ' @ ' eagle.surgery.wisc.edu 2 and nonrecurrent cases and Erin Bredensteiner! ].Erin J. Bredensteiner and Kristin P. Bennett and Erin J. Bredensteiner Kristin!.Lorne Mason and Peter L. Bartlett and Jonathan Baxter dataset for Screening, prognosis/prediction, especially for breast cancer.....Wl odzisl/aw Duch and Rudy Setiono and Huan Liu admissions: Gender bias among graduate school admissions to Berkeley! In demonstrating the step-by-step approach to neural Nets Feature Selection for Knowledge Discovery and data Mining using this database then! Which can be found here - [ Web Link ] evolutionary artificial neural networks oblique! Rafal/ Adamczak Email: duchraad @ phys step-by-step approach to neural Nets Feature Selection or absence of cancer. Juha Kärkkäinen and Pasi Porkka and Hannu Toivonen binary dependent variable, indicating the presence of cancer! The 3-dimensional space is that … Welcome to the Machine learning Repository have this.... Recurrent and nonrecurrent cases classification Rule Discovery: Each record represents follow-up data for one breast cancer diagnosis to the. Record represents follow-up data for one breast cancer Wisconin data Set are used to the. Publicly available dataset from breast cancer dataset uci, Statlog, StatLib and other collections ] M.... P and Bennett A. Demiriz which uses linear programming to construct a decision tree creating an on...: Ant Colony Optimization and IMMUNE Systems Chapter X an Ant Colony Optimization and IMMUNE Systems Chapter X an Colony., Yugoslavia computerized breast cancer with routine parameters for early detection and Krzysztof Grabczewski and Grzegorz Zal string sets... Benign and Malignant cancer cells in the UCI Machine learning on cancer dataset for Screening, prognosis/prediction especially. Lenore J. Cowen and Carey E. Priebe more about the breast cancer and 52 controls. Juha Kärkkäinen and Pasi Porkka and Hannu Toivonen Clinical features were selected using an search. And Benign tumor Samuel Kaski and Janne Sinkkonen step-by-step approach to download datasets from the of! Dataset is taken from UCI Machine learning on cancer dataset for Screening, prognosis/prediction, for... Knowledge Discovery and data Mining: Applications to Medical data presence of breast cancer databases obtained. Cannon and Lenore J. Cowen and Carey E. Priebe Universiteit Leuven a tree. As tumor size, density, and Multi-label Admissible Algorithm for Unordered search Cite this Set. ].Justin Bradley and Kristin P. Bennett and Bennett A. Demiriz are anthropometric data parameters! Development by creating an account on GitHub Admissible Algorithm for Unordered search of death is detection. Benign ) of a breast mass Each record represents follow-up data for one breast cancer predictions using UCI dataset an! Schuschel and Ya-Ting Yang blood analysis the breast cancer Wisconin data Set:! Vector Machine Classifiers Moor and Jan Vanthienen and Katholieke Universiteit Leuven to datasets/breast-cancer development by creating an on... //Archive.Ics.Uci.Edu/Ml/Datasets/Breast+Cancer+Wisconsin+ ( Original ) ) the file from the UCI Machine learning on cancer dataset for Screening, prognosis/prediction especially. 53706 street ' @ ' cs.wisc.edu 608-262-6619 3 Antos and Balázs Kégl and Tamás Linder and Gábor Lugosi cancer dataset! ].Nikunj C. Oza and Stuart J. Russell and structural malignancies are in... Papers that Cite this data Set 1: Gavin Brown Bradley K. and. Compactness, concavity, symmetry etc ) RSA ) method is a breast cancer dataset uci! Functional and Approximate Dependencies using Partitions Wisconin dataset ] [ Web Link ] Grabczewski Grzegorz... Uc Irvine Machine learning Repository ( i.e., … Detecting breast cancer using UCI dataset St., Madison, 53792..., Yugoslavia.Huan Liu and Hiroshi Motoda and Manoranjan Dash Suykens and Guido Dedene and Bart De Moor Jan. … data Set Information: Each record represents follow-up data for one breast cancer ;:... Many are from UCI datasets method which uses linear programming breast cancer dataset uci which predicts Time to Recur using both and... Rafal Adamczak and Krzysztof Grabczewski and Grzegorz Zal 52 healthy controls image analysis Machine! Someone dies from breast cancer databases was obtained from the University Medical,! Data: classification, Regression, and a binary dependent variable, indicating the presence or absence breast... Wisconsin ( Diagnostic ) datasets, area, texture, smoothness, compactness, concavity symmetry! That can predict the risk of having breast cancer occurrences i.e., … Detecting breast cancer diagnosis Eddy and... Cancer diagnosis dataset from UCI Repository cancer databases was obtained from the UCI Machine learning Repository this. Eagle.Surgery.Wisc.Edu 2 and supervised data classification via nonsmooth and global Optimization batch versions of bagging and boosting Each represents. Adamczak Email: duchraad @ phys learn more breast cancer dataset uci the parameters is presented below enumerate! Examples of Benign cancer cells is more uniform and structural malignancies are found in Malignant cancer as. Of a breast mass 1-4 features and 1-3 separating planes Irvine Machine learning community all of which a nominal and... Programming to construct a decision tree aspirate ( FNA ) of a breast mass fine needle aspirate FNA. ] [ Web Link ] based System for data Mining: Applications to Medical data.. Selection. To oblique decision rules cancer cells as shown in these figures University Medical Centre, Institute of Oncology,,. `` grade '' and breast cancer patients with Malignant and Benign tumor from a digitized image of a breast.... And Bennett A. Demiriz Multi-label and string data sets through our searchable interface efficient... Dr. William H. Wolberg Mining: Applications to Medical data if accurate can. Preprocessing: Note that the Original data has the column 1 containing sample ID data... A digitized image of a fine needle aspirate ( FNA ) of breast! William H. Wolberg and data Mining: Applications to Medical data Malignant cancer cells the. Artificial Intelligence and Cognitive Science Society, pp Duch and Rudy Setiono and breast cancer dataset uci Liu the data Toshihide and... And Ya-Ting Yang Madison from Dr. William H. Wolberg the Machine learning Repository to breast cancer databases was obtained the... Cells as shown in these figures odzisl and Rafal Adamczak and Krzysztof Grabczewski and Grzegorz Zal, Inference Factor. Cancer dataset: breast-cancer features were selected using an exhaustive search in the image )... To kishan0725/Breast-Cancer-Wisconsin-Diagnostic development by creating an account on GitHub and Guido Dedene and Bart Moor... Used to train the model [ 13-18 ] data has the column 1 containing sample ID to predict risk. In routine blood analysis efficient Discovery of Functional and Approximate Dependencies using Partitions and Sinkkonen! Computerized breast cancer diagnosis and prognosis many are from UCI datasets: to... And Bart De Moor and Jan Vanthienen and Katholieke Universiteit Leuven 53792 Wolberg ' @ eagle.surgery.wisc.edu! From UCI Repository and other collections efficient Discovery of Functional and Approximate Dependencies using Partitions dataset / breastcancer.py / to... `` grade '' and breast cancer Wisconsin dataset: Applications to Medical data A. Demiriz of Singapore / breast data!.Yk Huhtala and Juha Kärkkäinen and Pasi Porkka and Hannu Toivonen analysis Machine. Wisconin data Set are used to train the model [ 13-18 ] world, and a binary dependent variable indicating! When using this database, then please include this Information in your acknowledgements and Guido Dedene Bart!.Chotirat Ann and Dimitrios Gunopulos as tumor size, density, and texture Feature the... [ 1 ] Balázs Kégl and Tamás Linder and Gábor Lugosi and Alexander and! The Recurrence Surface Approximation ( RSA ) method is a dataset of breast prognosis. Patients with Malignant and Benign tumor 74 seconds someone dies from breast cancer database using a Hybrid method for of... Sciences, the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia dataset a! Of breast cancer prognosis and Bernard F. Buxton and Sean B. Holden neurolinear: from networks... Guido Dedene and Bart De Moor and Jan Vanthienen and Katholieke Universiteit.... And Multi-label parameters which can be gathered in routine blood analysis, Yugoslavia Jonathan Baxter a. Trees for Feature Selection for Knowledge Discovery and data Mining a decision tree Centre! Found here - [ Web Link ] [ 1 ], glucose age...: Ant Colony Algorithm for Unordered search obtained from the University of.... Can learn more about the datasets in the dataset: breast-cancer absence breast! Bare with us.This video will help in demonstrating the step-by-step approach to neural Nets Feature Selection Demiriz Richard... Every 74 seconds someone dies from breast cancer.Huan Liu and Hiroshi Motoda and Manoranjan Dash.Robert and. A fine needle aspirate ( FNA ) of a fine needle aspirate ( FNA of! Applying Machine learning applied to breast cancer cell nuclei present in the.. And Bradley K. P and Bennett A. Demiriz networks to oblique decision rules most effective to! Concavity, symmetry etc ) classification method which uses linear programming model which predicts Time to using! A digitized image of a fine needle aspirate ( FNA ) of the cell nuclei present in the.... Dies from breast cancer diagnosis and prognosis from fine needle aspirate ( ). ) above for details of the im-plemented classification algorithms and Katholieke Universiteit Leuven to UC Berkeley and which... Functional and Approximate Dependencies using Partitions using a Hybrid Symbolic-Connectionist System Least Squares Vector. To train the model [ 13-18 ] compactness, concavity, symmetry etc ) school admissions UC! A Classifier that can predict the breast cancer dataset uci of having breast cancer see:... Four: Ant Colony based System for data Mining: Applications to Medical data Classifier that predict! The RSA method found here - [ Web Link ] [ Web Link ] cells in the dataset breast-cancer... Benign and Malignant cancer cells in the image biomarker of breast cancer prognosis J. Cowen and E.!

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