no code implementations • 25 Jun 2021 • Noorul Wahab, Islam M Miligy, Katherine Dodd, Harvir Sahota, Michael Toss, Wenqi Lu, Mostafa Jahanifar, Mohsin Bilal, Simon Graham, Young Park, Giorgos Hadjigeorghiou, Abhir Bhalerao, Ayat Lashen, Asmaa Ibrahim, Ayaka Katayama, Henry O Ebili, Matthew Parkin, Tom Sorell, Shan E Ahmed Raza, Emily Hero, Hesham Eldaly, Yee Wah Tsang, Kishore Gopalakrishnan, David Snead, Emad Rakha, Nasir Rajpoot, Fayyaz Minhas
The analyses reported in this work highlight best practice recommendations that can be used as annotation guidelines over the lifecycle of a CPath project.
To train a robust deep learning model, one usually needs a balanced set of categories in the training data.
Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow.
Ranked #1 on Multi-tissue Nucleus Segmentation on Kumar
However, this task is non-trivial due to the large variability in glandular appearance and the difficulty in differentiating between certain glandular and non-glandular histological structures.
Ranked #3 on Colorectal Gland Segmentation: on CRAG
Distant metastasis is the major cause of death in colorectal cancer (CRC).