Author
Dr. Andreas Heindl
Dr Andreas Heindl is a Machine Learning Product Manager at Encord. He has spent the past 10 years applying computer vision and deep learning techniques in Healthcare at Encord, The Institute of Cancer Research, and Kheiron Medical. The main focus of Andreas' research and work until now has been to aid radiologists accurately diagnosing cancer by using artificial intelligence and computer vision.
All blogs by Dr. Andreas Heindl
Cross-Modal Learning: Leveraging Multiple Data Types for Better Models
4 m
Annotations for Explainable AI: Building Interpretable Models
4 m
Monitoring and Managing Data Drift in Production ML Systems
4 m
Estimating Annotation Projects: Timeline, Cost, and Resource Planning
4 m
AI Platforms Data Management: The Definitive Enterprise Guide
4 m
The Definitive Object Tracking Handbook for 2026
5 m
The Ultimate Human-in-the-Loop Guide for 2026
4 m
Achieving Annotation Consensus: Strategies for High-Agreement Datasets
4 m
Debugging Image Processing: Common Issues and Solutions
4 m
Annotating Time Series Data for Predictive AI Models
5 m
Workflow Management Technical Deep Dive
4 m
Ontology Architecture and Implementation Guide
6 m
How to Master Model Evaluation: A Step-by-Step Tutorial
4 m
Best Tools for Labeling 3D Files in 2025
8 m
Implementing Active Learning Loops: From Theory to Production
3 m
Understanding Model Evaluation: Technical Documentation
4 m
Best Tools for Labeling 3D Files in 2026
8 m
Test Blog Post Created via API
3 m
Best Tools for Labeling 3D Files in 2026
8 m
Best Tools for Labeling 3D Files in 2026
8 m
Deploying Computer Vision Models at the Edge: Data Considerations
4 m
Complete Guide to Embeddings in 2026
3 m
7 Ways to Improve Medical Imaging Dataset
7 m
The Comprehensive Guide to Medical Annotations
10 m
What’s the Difference Between DICOM and NIfTI?
5 m
Audio Annotation for AI: From Speech to Sound Recognition
4 m
Measuring and Improving Annotation Quality: Metrics That Matter
3 m
Zero-Shot Classification: Building Models That Generalize to New Classes
5 m
Document AI: From OCR to Intelligent Data Extraction
5 m
Continuous Learning in Production: Keeping Your Models Current
4 m
Complete Guide to Quality Assurance in 2026
3 m
Geospatial Data Annotation: Satellite and Aerial Imagery Analysis
4 m
Complete Guide to Annotation Workflow in 2026
4 m
Leveraging Synthetic Data: When and How to Use Generated Training Data
5 m
Integrating Custom Models into Your Annotation Pipeline
5 m
Annotation Automation: Calculating Real ROI and Implementation Strategies
6 m
Annotation Efficiency: A Comprehensive Guide for Teams
4 m
Data Annotation for Robotics: From Simulation to Real-World Deployment
4 m
Advanced Video Annotation: Temporal Tracking and Action Recognition
4 m
Everything About Human-in-the-Loop: Complete Guide
4 m
Pre-labeling Architecture and Implementation Guide
4 m
Computer Vision for Agriculture: Handling Variable Field Conditions
5 m
Everything About Audio Annotation: Complete Guide
5 m
The Top 6 Artificial Intelligence Healthcare Trends of 2024
7 m
Future for Computer Vision in Healthcare
7 m
7 Features to Look for in a DICOM Annotation Tool
6 m
How To Obtain CE Approval for Medical Diagnostic Models
8 m
How to Structure QA Workflows for Medical Images
12 m
How to Annotate DICOM and NIfTI Files
5 m
Best DICOM Annotation Tools for Radiology AI [2024 Review]
6 m
6 Best Open Source DICOM Annotation Tools
10 m
Top 10 Free Healthcare Datasets for Computer Vision
6 m
Best LiDAR Annotation Platforms (2025): Fast, Accurate, Sensor-Fusion Ready
Best Data Labeling Platform (2025 Buyer’s Guide)
Best DICOM Annotation Platforms (2025): Radiology-Grade Tools
Best Image Labeling Platforms (2025): When Encord Wins and What Else to Consider
The Step-by-Step Guide to Getting Your AI Models Through FDA Approval
10 m