How to Annotate Consistently | Data Labeling best practices for Pathology Images

This video is brought to you by Aiforia: https://www.aiforia.com/

Self-discipline is an essential trait for data labeling and developing a robust deep-learning model for pathology. In supervised deep learning, it is crucial to have consistent and accurate ground truth data for the model to learn from. This requires a high level of self-discipline and attention to detail throughout the tissue image analysis model development process.

Fortunately, there are tools available to help make this process more manageable. Aiforia is one such platform that allows for efficient and accurate annotation of data. Thanks to their sponsorship, users can access the platform and learn how to use it to improve their supervised deep-learning projects.

By utilizing the right tools and demonstrating self-discipline, researchers can create deep learning models for pathology that are highly accurate. With the help of Aiforia, achieving this level of success is within reach. So, if you want to take your tissue image analysis deep learning projects to the next level, consider incorporating self-discipline and leveraging powerful tools like Aiforia to achieve your goals.

//CHAPTERS:
00:00 - 00:20 - Introduction
00:21 - Tissue Annotation Challenges
0:56 - Drifting while Annotating
1:28 - How do you keep yourself in check for the consistent ground truth?
1:35 - 1st Keypoint: Decide what you're going to be annotating
1:54 - Special thanks to Aifria for sponsoring this video
2:01 - Annotating mouse lung cancer model tissue
2:43 - 2nd Keypoint: Start Annotating
3:39 - 3rd Keypoint: Train your 1st model
4:12 - Question to be answered: Does this model underperform?
5:52 - Recap
6:53 - Full webinar link to the Aiforia annotations: https://digitalpathologyplace.com/annotations-tips-and-tricks-how-to-develop-the-ground-truth-for-a-deep-learning-ai-model/

FREE RESOURCES FOR DIGITAL PATHOLOGY TRAILBLAZERS:

📘 Digital Pathology 101 E-book!
https://digitalpathology.club/digital-pathology-beginners-guide-notification

This is hands down the BEST place to start when it comes to digital pathology that will bring you up to speed or structure your current knowledge and launch your expertise. Don't miss it!

=================================

NEXT TIER RESOURCES FOR DIGITAL PATHOLOGY TRAILBLAZERS:

💻 Pathology 101 for non-pathologists (within the Digital Pathology Club)
https://digitalpathology.club/dp-club-membership-sp
Are you starting your journey in tissue image analysis and computational pathology and are confused about what exactly you are supposed to analyze on the whole slide image?
I am finishing a course for computer scientists and professionals starting their journey in image analysis for pathology. This course will help you understand tissue and pathology without the necessity to deeply understand medicine. Join now!

=================================

RECOMMENDED DIGITAL PATHOLOGY THINGS:

📱 My microscopic photography phone case: http://skopedmicro.com/1625068689/digitalpathologyplace
📷 My microscope camera (It's amazing!!!): https://imillermicroscopes.com/pages/path4k

=================================

LET'S CONNECT ON SOCIAL:

🌎 Website: https://digitalpathologyplace.com/
#️⃣ Instagram: https://www.instagram.com/digital_pathology_place/
#️⃣ FB: https://www.facebook.com/digitalpathologyplace
#️⃣ LinkedIn: https://www.linkedin.com/in/aleksandra-zuraw-dvm-phd-dacvp/

=================================

Видео How to Annotate Consistently | Data Labeling best practices for Pathology Images канала Aleksandra Zuraw Digital Pathology Place