Blog

ImageBiopsy Lab uses model-assisted labeling to increase efficiency by 160%

ImageBiopsy Lab builds AI applications to help doctors better diagnose musculoskeletal diseases.

Genentech develops breakthrough labeling process for medical imagery ML

Genentech researchers are building convolutional neural networks to help diagnose illnesses and aid medical professionals.

How to improve model performance with active learning

Let's dive into some of the specific challenges of data-centric ML and how your team can address them by employing active learning methods.

How to build trust in your machine learning models

Getting buy-in from stakeholders can be a tough challenge for ML teams who want to build transformative, enterprise models that support major operations.

Labelbox lands in London: The key to a performant ML model lies within your training data

In a data-centric approach to machine learning, no element is more essential in your ML endeavors than creating and maintaining high-quality training data. Many ML teams fail due to two common but critical mistakes.

Labelbox now supports integrations with all major cloud providers + HIPAA compliance

Announcing HIPAA compliance and data integrations with all major cloud providers: Amazon AWS, Google Cloud, and Microsoft Azure.

What to Expect at Labelbox Accelerate 2021

If you have an interest in how machine learning is changing businesses and lives, and how you can transform your own AI/ML operations, Labelbox Accelerate 2021 is a must-attend event. Here are a few things you can expect to see at Labelbox Accelerate 2021.

How to get the most out of your labeling operations

Teams that monitor and refine their labeling operations realize significant savings in time and costs, and can more accurately estimate future requirements.

Summer 2021 Product Updates

We've released a host of updates to all label editors, unlocking more advanced use cases for video, text, image, and tiled imagery, and have further improved the capabilities of our SDK.

New study finds that non-expert labeling teams can create high quality training data for medical use cases

Imperial College London’s BioMedIA research group and the iFind Project set out to challenge the assumption that only those medical training can provide quality annotations for clinical deep learning models.