DSS WEBINARS
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Upcoming webinars
De-Identification of Medical Images in DICOM Format
Watch live: Wednesday, November 13 @ 2pm 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
John Snow Labs
Top-10 Misconceptions About LLM Judges in Production
Watch live: Wednesday, December 4 @ 2pm ET
Implementing LLM “judges” in production settings can be a game-changer for evaluating AI behaviors, but it’s often more challenging than it appears. Many teams struggle with common pitfalls such as; high error rates and cost unpredictability to issues with latency and long-term maintenance. This webinar will break down the top 10 misconceptions around LLM judges, equipping you with the insights to avoid these challenges and build more reliable, production-ready evaluation systems.
Join us to learn:
• Key Misconceptions: Understand where teams often go wrong in deploying LLM judges.
• EvalOps Principles: Discover best practices and tools to operationalize evaluations effectively.
• Reliability in Production: Learn how to make evaluation outcomes systematic, scalable, and dependable.
• Use Cases & Value: Explore real-world applications where LLM judges add significant value and the tangible results they deliver.
Who Should Attend:
This webinar is ideal for machine learning engineers, data scientists, AI practitioners, and technical leaders seeking to enhance their approach to model evaluation in production environments.
Ari Heljakka, PhD
Founder & CEO
Root Signals
Ouz Gencoglu
Co-Founder & Head of AI
Root Signals
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