3 citations · 7 across the 6 of their papers we have counts for
6 papers
Unlocking Diffusion Hierarchies: Adaptive Timestep Selection for Zero-Shot Segmentation
Ramin Nakhli, Mahesh Ramachandran, Luca Ballan
Zero-shot segmentation has recently shown notable improvement by leveraging the rich visual priors in large-scale text-to-image diffusion models, such as Stable Diffusion. However,…
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…
HexaGen3D: StableDiffusion is just one step away from Fast and Diverse Text-to-3D Generation
Antoine Mercier, Ramin Nakhli, Mahesh Reddy +4
Despite the latest remarkable advances in generative modeling, efficient generation of high-quality 3D assets from textual prompts remains a difficult task. A key challenge lies in…
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…
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…