Home » Register for Webinar: De-Identification of Medical Images in DICOM Format
REGISTER FOR OUR NEXT WEBINAR
De-Identification of Medical Images in DICOM Format
November 13, 2024 @ 2:00 PM ET
De-identification of medical records is crucial for unlocking valuable information for several reasons: Privacy, compliance, enabling medical research, and reducing the risk of data breaches. DICOM is a widely-used file format standard for exchanging medical images such as radiography, ultrasonography, computed tomography (CT), magnetic resonance imaging (MRI), and radiation therapy. Accurate anonymization of DICOM files presents unique challenges:
Sensitive information is often “burned” into the image, which requires computer vision or OCR to identify
Sensitive information is also stored in metadata fields, some of which include unstructured text
The DICOM standard is decades old, hence there are thousands of variants of file formats and metadata fields
Each DICOM file can contain thousands of images (slices), in different resolutions
Different image modalities (MRI vs. US vs. CT scans) have their own nuances
This session presents a scalable, enterprise-grade solution that provides high accuracy across supporting multiple image formats and clinical modalities. Join to see live demos & code that tackles these challenges with the help of John Snow Labs’ Visual NLP. We’ll will explore DICOM processing capabilities, from computing basic metrics on a potentially large dataset to de-identifying images and metadata. We will also discuss infrastructure and how to scale pipelines to handle heavy workloads.
Alberto Andreotti
Senior Data Scientist at John Snow Labs
Alberto Andreotti is a data scientist at John Snow Labs, specializing in Machine Learning, Natural Language Processing, and Distributed Computing. With a background in Computer Engineering, he has expertise in developing software for both Embedded Systems and Distributed Applications.
Alberto is skilled in Java and C++ programming, particularly for mobile platforms. His focus includes Machine Learning, High-Performance Computing (HPC), and Distributed Systems, making him a pivotal member of the John Snow Labs team.