Machine Learning

Can CNNs reliably identify cancerous cells?

Acute Lymphoblastic Leukemia (ALL) is the most common pediatric cancer and the most frequent cause of death from cancer before 20 years of age. In the 1960s ALL had a survival rate of only 10%, but advancements in diagnostic testing and refinements to chemotherapies have increased survival rates to 90% in developed countries. [1]

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Image Courtesy of Paweł CzerwińskiUnsplash

Researchers are attempting a variety of personalized approaches, mainly using epigenetic screenings and genome-wide association studies (GWAS) to identify potential targets for inhibition, to push survival rates even higher. …

Machine Learning

NAT may be the Next Big Thing in Deep Learning

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A possible network architecture. Image by author.

Neural network topology describes how neurons are connected to form a network. This architecture is infinitely adaptable, and novel topologies are often hailed as breakthroughs in neural network research. From the advent of the Perceptron in 1958 to the Feed Forward neural network to Long/Short Term Memory models to — more recently — Generative Adversarial networks developed by Ian Goodfellow, innovative architectures represent great advancements in machine learning.

But how does one find novel, effective architectures for specific problem sets? Solely through human ingenuity, until recently. This brings us to Neural Architecture Search (NAS), an algorithmic approach to discover optimal network topologies through raw computational power. The approach is basically a massive GridSearch. Test many combinations of hyperparameters, such as number of hidden layers, number of neurons in each layer, activation function, etc., …

Data Science

Data science may be the future of personal medicine

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Marcelo Leal on Unsplash

Personalized, or precision, medicine has long been a touchstone for what the future of treatment could be. It consists of using knowledge specific to a patient, such as biomarkers, demographics, or lifestyle characteristics, to best treat their ailment, rather than generic, averaged best practices. In the best case, it could leverage the expertise of thousands of practitioners and outcomes from millions of other patients to provide proven, effective care.

Precision medicine has a number of benefits, both for the patient and physician (adapted from Fröhlich):

  1. Improved treatment efficacy
  2. Reduced adverse effects
  3. Lower costs for patient and providers
  4. Earlier diagnosis using…


Aren Carpenter

Data Scientist. Exploring the intersection between AI and Healthcare/Oncology.

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