In the following examples, we will work with these two transformers: DicomToImageV3, responsible for extracting frame images, and DicomDrawRegions, which draws rectangle regions to the frames and proves useful in building de-identification pipelines.
This post will explore how Visual NLP can manipulate pixel and overlay data within DICOM images. In the following examples, we will work with these transformers: DicomToImageV3, responsible for extracting...
Start to work with DICOM in Visual NLP In this post, we are deeply diving into working with metadata using Visual NLP. We are going to make use of Visual...
Introduction In the world of healthcare and medical research, the ability to access and share medical images is crucial for diagnosis, treatment, and scientific investigation. However, these images often contain...
The latest version of the library fixed some relevant errors on the deidentification pipelines on financial documents. With the fixes, the library is fully compatible with newer versions of Spark....
This talk examines the crucial need for de-identifying protected health information (PHI) in unstructured patient-level data to harness its potential while ensuring compliance with legal and privacy requirements. With an...
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