If you are interested in creating your own medical image data set for a machine learning project, this post is for you. You may also be interested in this post if you work with publicly-available medical imaging datasets and would like some further insight into how these datasets are created.
In this post, you will learn:
This tutorial is based on my repository pytorch-computer-vision which contains PyTorch code for training and evaluating custom neural networks on custom data. By the end of this tutorial, you should be able to:
The repository with all the code is https://github.com/rachellea/pytorch-computer-vision
At the end of this tutorial you should be able to:
There are many pre-defined CNN models provided in PyTorch, including:
This post describes best practices for organizing machine learning projects that I have found to be highly effective during my PhD in machine learning.
Python is a great language for machine learning. Python includes a bunch of libraries that are super useful for ML:
This post delves in to the use of CT scans in the COVID-19 pandemic, including current guidelines from medical experts (as of August 2020) and examples of recent research papers that use machine learning to make predictions from CT scans of COVID-19 patients.
Disclaimer: Nothing in this post is medical advice.
The gold standard for diagnosis of COVID-19 is reverse transcription polymerase chain reaction (RT-PCR), which is a laboratory test that detects genetic material (RNA) from the COVID-19 virus:
This post provides an overview of chest CT scan machine learning organized by clinical goal, data representation, task, and model.
A chest CT scan is a grayscale 3-dimensional medical image that depicts the chest, including the heart and lungs. CT scans are used for the diagnosis and monitoring of many different conditions including cancer, fractures, and infections.
The clinical goal refers to the medical abnormality that is the focus of the study. The following figure illustrates some example abnormalities, shown as 2D axial slices through the CT volume:
Convolutional neural networks (CNNs) are the most popular machine leaning models for image and video analysis.
Here are some example tasks that can be performed with a CNN:
In a CNN, a convolutional filter slides across an image to produce a feature map (which is labeled “convolved feature” in…
This post offers the clearest explanation on the web for how the popular metrics AUC (AUROC) and average precision can be used to understand how a classifier performs on balanced data, with the next post focusing on imbalanced data. This post includes numerous simulations and AUROC/average precision plots for classifiers with different properties. All code to replicate the plots and simulations is provided on GitHub.
First, here is a brief intro to AUROC and average precision:
The AUROC indicates whether your model can correctly rank examples. The AUROC is the probability that a randomly selected positive example has a higher…
Grad-CAM is a popular technique for visualizing where a convolutional neural network model is looking. Grad-CAM is class-specific, meaning it can produce a separate visualization for every class present in the image:
Your genome is approximately 750 megabytes of information (3 x 10⁹ letters x 1 byte/4 letters). That’s about half the size of an operating system except it codes for an entire human body, and the entire code fits into a volume a hundred times smaller than a grain of rice. Your brain, which develops as specified by your genome, is an incredible supercomputer that also happens to require less power than a dim light bulb — literally tens of thousands of times more energy-efficient than manmade supercomputers.
So how does the genome work?
Genomics, transcriptomics, and proteomics are data-driven fields…