Encord Monthly Wrap: January Industry Newsletter

Stephen Oladele
February 1, 2024
8 min read
blog image

Welcome to the January 2024 edition of Encord's Monthly Wrap.

It’s also our chance to wish you a belated happy new year! 

Here’s what you should expect:

  • Two interesting computer vision papers we reckon you check out.
  • Hands-on tutorials you can work on during weekends.
  • Developer resources you should bookmark, including Colab Notebooks.
  • Computer vision use cases in manufacturing and robotics.
  • Power tip for computer vision data explorers.

Let’s dive in!

Top Picks for Computer Vision Papers You Should See

Segment Anything in Medical Images (MedSAM)

This paper presents MedSAM, a novel adaptation of the Segment Anything Model (SAM) specifically for medical images.

What’s impressive? 🤯

  • It introduces a large-scale medical image dataset with over 200,000 masks across 11 modalities and utilizes a fine-tuning method to adapt SAM for general medical image segmentation. 
  • It demonstrates superior performance over the original SAM, significantly improving the Dice Similarity Coefficient on 3D and 2D segmentation tasks.

MedSAM - Encord

There’s also an accompanying repository with a shoutout to one of our pieces on fine-tuning SAM 😉.

CLIP in Medical Imaging: A Comprehensive Survey 

This survey explores the Contrastive Language-Image Pre-Training (CLIP) application in the medical imaging domain. It delves into the adaptation of CLIP for image-text alignment and its implementation in various clinical tasks.

What’s impressive? 👀

  • It provides an in-depth analysis of CLIP's utility in medical imaging, covering the challenges of adapting it to the specific requirements of medical images.
  • It shows how well CLIP generalizes tasks like 2D and 3D medical image Fsegmentation, medical visual question answering (MedVQA), and generating medical reports.

Illustration of CLIP's generalizability via domain identification | Encord

Illustration of CLIP’s generalizability via domain identification

DICOM & NIfTI Editor | Encord

Medical professionals use Encord’s DICOM & NIfTI Editor to quickly label large training datasets across modalities such as CT, X-ray, ultrasound, mammography, and MRI.

Collaborative DICOM annotation platform for medical imaging
CT, X-ray, mammography, MRI, PET scans, ultrasound
medical banner

Want to get hands-on? Check Out These Computer Vision Tutorials

Computer Vision Tutorial | Encord

Developer Resources You’d Find Useful

Bulk Classification | Encord

Practical Computer Vision Use Cases

Computer Vision Application in Manufacturing | Encord

  • Top 8 Use Cases of Computer Vision in Manufacturing → This article discusses the diverse applications of computer vision across various manufacturing industries, detailing their benefits and challenges, from product design and prototyping to operational safety and security.
  • Top 8 Applications of Computer Vision in Robotics → This article explores computer vision applications in the robotics domain and mentions key challenges the industry faces today, from autonomous navigation and mapping to agricultural robotics.

Top 3 Resources by Encord in January

  1. How to Adopt a Data-Centric AI → For data teams to succeed in the long term, they must use high-quality data to build successful AI applications. But what is the crucial sauce for building successful and sustainable AI based on high-quality data? A data-centric AI approach! We released this whitepaper to guide you on how to develop an effective data-centric AI strategy.
  2. Top 15 DICOM Viewers for Medical Imaging → In the market for a DICOM viewer? We published a comparison article that discusses what to look for in an ideal viewer and the options in the market so you can make the optimal choice.
  3. Instance Segmentation in Computer Vision: A Comprehensive Guide → We published an all-you-need-to-know guide on instance segmentation, including details on techniques like single-shot instance segmentation and transformer- and detection-based methods. We also cover the U-Net and Mask R-CNN architectures, practical applications of instance segmentation in medical imaging, and the challenges.

Our Power Tip of the Month

If you are trying to become a computer vision data power user, I’ve got a tip to help supercharge your exploration gauntlet (I see you, Thanos 😉).

Within Encord Active, you can see the metric distribution of your data to identify potential data gaps that could influence model performance on outliers or edge cases. Here’s how to do it in 3 steps on the platform: Analytics >> Scroll down to Metric Distribution >> Choose a pre-built or custom Metric, and observe!

Distribution Metric - Encord

Good stuff 🤩. I hope you find it useful. Here are other quick finds if you 💓 Encord and computer vision data stuff ⚡:

Till next month, have a super-sparkly time!

author-avatar-url
Written by Stephen Oladele
Stephen Oladele is a Developer Advocate and an MLOps Technical Content Creator at Encord. He has significant experience building and managing data communities, and you will find him learning and discussing machine learning topics across Discord, Slack and Twitter. Linkedin Twitte
View more posts
cta banner

Automate 97% of your annotation tasks with 99% accuracy

Learn more
cta banner

Discuss this blog on Slack

Join the Encord Developers community to discuss the latest in computer vision, machine learning, and data-centric AI

Join the community

Software To Help You Turn Your Data Into AI

Forget fragmented workflows, annotation tools, and Notebooks for building AI applications. Encord Data Engine accelerates every step of taking your model into production.