3 citations · 4 across the 3 of their papers we have counts for
3 papers
GRASP: GRAph-Structured Pyramidal Whole Slide Image Representation
Ali Khajegili Mirabadi, Graham Archibald, Amirali Darbandsari +9
Cancer subtyping is one of the most challenging tasks in digital pathology, where Multiple Instance Learning (MIL) by processing gigapixel whole slide images (WSIs) has been in the…
VOLTA: an Environment-Aware Contrastive Cell Representation Learning for Histopathology
Ramin Nakhli, Allen Zhang, Hossein Farahani +8
In clinical practice, many diagnosis tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques require labels, providing manu…
CCRL: Contrastive Cell Representation Learning
Ramin Nakhli, Amirali Darbandsari, Hossein Farahani +1
Cell identification within the H&E slides is an essential prerequisite that can pave the way towards further pathology analyses including tissue classification, cancer grading, and…