In this Python3 code example, a set of MRI images from one scanning session is resampled to match the imaging axis of a reference scan. The DICOM images are handled using PyDICOM. The resampling function is implemented using SimpleITK.
An example use case for this code example is as follows. Consider two MRI scans, A and B, with scan A imaged along the sagittal (longitudinal) plane and scan B along the axial (horizontal) plane.
If the reference image is set as scan A, then the scan B images will be transformed to appear as image slices along the sagittal plane; instead of the original axial plane used during the scan acquisition.
Similarly, if the reference image is set as scan B, then the scan A images will be transformed to appear as image slices along the axial plane; instead of the original sagittal plane used during the scan acquisition.
The example notebook is hosted in Kaggle. The MRI scans used in this notebook are from the RSNA MICCAI brain tumor classification dataset.
This blog article on Twitter saliency filter analysis introduces a few broad concepts to test machine vision tools. Described here is an end-to-end automated statistical analysis tool that is used to analyze the Twitter saliency filter. The aim is to accelerate the development of scalable, automated testing of machine vision algorithms for possible biases.
1. Create 'main' branch
$ git branch -m master main
A new branch called 'main' is created using the 'git branch [branch] [newbranch]' command. By passing the '-m ' argument to this git branch management command, the commit histories are copied to the new branch.
I often tell my students not to be misled by the name 'artificial intelligence'. There is nothing artificial about it. AI is made by humans, intended to behave by humans, and, ultimately, to impact humans' lives and human society.
-- Fei-Fei Li, the inaugural Sequoia Professor in the Computer Science Department at Stanford University, and Co-Director of Stanford's Human-Centered AI Institute
One of the common mistakes made by decision makers during the process of incorporating artificial intelligence into their enterprise, is attempting to view artificial intelligence as either good or bad. I want to explain here why this approach could be an indication of fundamental ignorance about artificial intelligence systems.
"... Not one would mind, neither bird nor tree
If mankind perished utterly;
And Spring herself, when she woke at dawn,
Would scarcely know that we were gone."
-- Sara Teasdale, There Will Come Soft Rains
This year marks the 35th anniversary of Chernobyl disaster. On 26th April, 1986, at 1:23:45 AM local time (21:23:45 UTC), the 1000 ton concrete lid located above the reactor 4 fuel elements of the Chernobyl nuclear power plant in the city of Pripyat, erstwhile Ukraine Soviet Socialist Republic (SSR), was blown open to the side by a massive steam explosion.
Bazel is the build and test automation tool from Google. It is touted as a fast, scalable, multi-language and extensible build system. Bazel is needed to create Tensorflow installers from the source-code. Here are the steps to install Bazel in Linux, from the source-code. The technique described here uses the bootstrap installation that does not require a pre-existing Bazel installation in the target system.
Working with large datasets in Python can often result in memory utilization issues. To reduce the memory utilization, the variables that are no longer immediately needed can be written to the disk. These variables can then be removed from the memory, thereby reducing the memory utilization. When these variables are needed at a later stage in the program, it can be easily restored from the disk. This strategy works for majority of the use cases, where the disk space is cheaper and more readily available than the main memory. In Python, restoring a variable from the disk can be efficiently handled using Pickle. Here is a concise Python3 code example for using Pickle to manage memory utilization, by using the strategy of restoring a variable from the disk.
Moad Computer is an actionable insights firm. We provide enterprises with end-to-end artificial intelligence solutions. Actionable Insights blog is a quick overview of things we are most excited about.