Obstetrics/Gynecology


Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders. - PubMed - NCBI

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While both supervised learning algorithms and unsupervised clustering algorithms have been successfully applied to biological data, they are either dependent on known biology or limited to discerning the most significant signals in the data. We evaluate the performance of DAs by applying them to a large collection of breast cancer gene expression data. Results show that DAs successfully construct features that contain both clinical and molecular information. In summary, we demonstrate that DAs effectively extract key biological principles from gene expression data and summarize them into constructed features with convenient properties.


Predicting Breast Cancer Using Apache Spark Machine Learning Logistic Regression

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Then we use another map transformation, which will apply the ParseObs function to transform each Array of Double in the RDD into an Array of Cancer Observation objects. The toDF() method transforms the RDD of Array[[Cancer Observation]] into a Dataframe with the Cancer Observation class schema. Below the data is split into a training data set and a test data set, 70% of the data is used to train the model, and 30% will be used for testing. In this blog post, we showed you how to get started using Apache Spark's machine learning Logistic Regression for classification.


K-Nearest Neighbors on Wisconsin Breast Cancer Data

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There are 357 cells that were identified as benign and 212 that were identified as malignant. In the context of the kNN algorithm that we will be running, this means that the maximum error rate that will occur is, on average, going to be 37.3%. If each test observations is classified as the majority label for all training points, they will all be classified as benign, with an error rate of how ever many malignant cells there are in the testing sample. This will be, on average, 37.3% if our test set is chosen randomly.


Google AI detects breast cancer better than pathologists - Pharmaphorum

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Google has successfully applied deep learning artificial intelligence algorithms to the diagnosis of breast cancer. In a study carried out by researchers taking part in Google's Brain Residency Program – a 12-month educational course in machine and deep learning – an algorithm was trained to detect breast cancer tumours in a dataset of digitised pathology slides provided by Dutch medical institute the Radboud University Medical Center. After'training' the algorithm, researchers were able to achieve a 92% sensitivity in picking out tumour cells from the slides – significantly higher than the 73% achieved by trained pathologists with no time constraint. Israel-based Zebra Medical is a company solely dedicated to the AI application, housing an arsenal of different deep learning algorithms to help diagnose a range of different disorders, from spinal fractures to breast cancer.


Google's Machine Learning, Imaging Analytics Flag Breast Cancer

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"We envision that algorithm such as ours could improve the efficiency and consistency of pathologists," said Stumpe and Peng. "For example, pathologists could reduce their false negative rates (percentage of undetected tumors) by reviewing the top ranked predicted tumor regions including up to 8 false positive regions per slide. As another example, these algorithms could enable pathologists to easily and accurately measure tumor size, a factor that is associated with prognosis."


Google uses AI to help diagnose breast cancer

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Google announced Friday that it has achieved state-of-the-art results in using artificial intelligence to identify breast cancer. Google used a flavor of artificial intelligence called deep learning to analyze thousands of slides of cancer cells provided by a Dutch university. With 230,000 new cases of breast cancer every year in the United States, Google (GOOGL, Tech30) hopes its technology will help pathologists better treat patients. His university provided the slides for Google's research.


Google uses AI to help diagnose breast cancer

#artificialintelligence

Google announced Friday that it has achieved state-of-the-art results in using artificial intelligence to identify breast cancer. Google used a flavor of artificial intelligence called deep learning to analyze thousands of slides of cancer cells provided by a Dutch university. With 230,000 new cases of breast cancer every year in the United States, Google (GOOGL, Tech30) hopes its technology will help pathologists better treat patients. His university provided the slides for Google's research.


Feature Selection in Machine Learning (Breast Cancer Datasets)

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And once you've got a feel for your data, investing the time and effort to compare different feature selection methods (or engineered features), model parameters and - finally - different machine learning algorithms can make a big difference! Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Now that I have a general idea about the data, I will run three feature selection methods on all three datasets and compare how they effect the prediction accuracy of a Random Forest model. Now I can compare Random Forest models with the different feature subsets.


Young Genius Makes Breast Cancer Diagnosis Less Painful

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Women who are diagnosed early have a 95 percent chance of living at least five years after diagnosis and it's estimated early breast cancer diagnosis could save 400,000 lives globally each year, according to the World Health Organization. FNAs are currently less reliable in conclusively diagnosing breast cancer than more invasive and painful techniques. Meet Brittany Wenger of Lakewood Ranch, FL, the creator of the Global Neural Network Cloud Service for Breast Cancer, the tool that is making it all possible. With ANN, patterns too complex for a human or program to even notice can correctly detect breast cancer lesions 99.1 percent of the time.


Pigeons diagnose breast cancer on X-rays as well as radiologists

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"Pigeons do just as well as humans in categorizing digitized slides and mammograms of benign and malignant human breast tissue," said Richard Levenson, professor of pathology and laboratory medicine at UC Davis Health System and lead author of a new open-access study in PLoS One by researchers at the University of California, Davis and The University of Iowa. The birds were remarkably adept at discriminating between benign and malignant breast cancer slides at all magnifications, a task that can perplex inexperienced human observers, who typically require considerable training to attain mastery. "The birds were remarkably adept at discriminating between benign and malignant breast cancer slides at all magnifications, a task that can perplex inexperienced human observers, who typically require considerable training to attain mastery," Levenson said. This work also suggests that pigeons' remarkable ability to discriminate between complex visual images could be put to good use as trained medical image observers, to help researchers explore image quality and the impact of color, contrast, brightness, and image compression artifacts on diagnostic performance.