Deep Learning for Whole Slide Imaging | Preprocessing and Annotating WSI Datasets with V7
Whole slide imaging (WSI) is a technique in medical imaging that involves scanning an entire glass slide containing biological tissue at high magnification and digitizing it into a single image. These images can be as large as four gigabytes in size and contain a vast amount of cellular-level information.
Working with WSI files poses some challenges, especially if we want to use them as ML training data. In this video we’ll discuss how to use V7 for digital pathology and WSI tissue samples.
0:00 - Intro
0:35 - What is whole slide imaging?
2:08 - The challenges of working with WSI samples
4:25 - Preparing the dataset and uploading it to V7
5:36 - Annotating the WSI file
7:18 - Final thoughts
V7 provides a straightforward interface that allows users to upload their WSI images and label regions of interest. This annotation process is essential to building accurate AI models that can analyze whole slide images at scale.
To learn more and explore the platform, visit:
Get Started: http://go.v7labs.com/41pK3WY
Find Out More: http://go.v7labs.com/3KWCl07
How V7 is being used to speed up tumor detection:http://go.v7labs.com/3GIls7L
How to annotate Digital Pathology images with V7: http://go.v7labs.com/3L9w8zf
Reference Pages:
https://www.mbfbioscience.com/whole-s...
https://www.iis.fraunhofer.de/en/ff/s...
Data: https://www.kaggle.com/datasets/marca...
https://www.kaggle.com/datasets/marca...
The dataset is 13-18GB, takes about 30 mins-60 mins to download and unzip.