8 citations · 17 across the 5 of their papers we have counts for
5 papers
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…
AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images
Ramin Nakhli, Puria Azadi Moghadam, Haoyang Mi +4
Processing giga-pixel whole slide histopathology images (WSI) is a computationally expensive task. Multiple instance learning (MIL) has become the conventional approach to process…
A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images
Puria Azadi Moghadam, Sanne Van Dalen, Karina C. Martin +4
Visual microscopic study of diseased tissue by pathologists has been the cornerstone for cancer diagnosis and prognostication for more than a century. Recently, deep learning metho…
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…
Classification of Epithelial Ovarian Carcinoma Whole-Slide Pathology Images Using Deep Transfer Learning
Yiping Wang, David Farnell, Hossein Farahani +6
Ovarian cancer is the most lethal cancer of the female reproductive organs. There are major histological subtypes of epithelial ovarian cancer, each with distinct morphological…