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 delve into the utilization of Visual NLP to manipulate pixel and overlay data within DICOM images. In the following examples, we will work with these two transformers:...
Dandelion Health is a provider of multimodal, longitudinal clinical data for healthcare innovators. This session shows how it built a de-identification process for free-text clinical notes, with John Snow Labs’...
Overall, de-identification in today’s data-driven world is a critical practice that helps balance the benefits of AI and big data with the need for privacy and compliance, facilitating both technological...
Redesign of embedding models Recent developments in NLP rely on vector representations of text, commonly known as embeddings. To support the utilization, training, and fine-tuning of models for the legal...
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...
Demos
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