10 citations · 16 across the 5 of their papers we have counts for
5 papers
SPLICE -- Streamlining Digital Pathology Image Processing
Areej Alsaafin, Peyman Nejat, Abubakr Shafique +4
Digital pathology and the integration of artificial intelligence (AI) models have revolutionized histopathology, opening new opportunities. With the increasing availability of Whol…
Analysis and Validation of Image Search Engines in Histopathology
Isaiah Lahr, Saghir Alfasly, Peyman Nejat +16
Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient matching for various purposes, ranging from triaging and d…
Selection of Distinct Morphologies to Divide & Conquer Gigapixel Pathology Images
Abubakr Shafique, Saghir Alfasly, Areej Alsaafin +3
Whole slide images (WSIs) are massive digital pathology files illustrating intricate tissue structures. Selecting a small, representative subset of patches from each WSI is essenti…
Rotation-Agnostic Image Representation Learning for Digital Pathology
Saghir Alfasly, Abubakr Shafique, Peyman Nejat +4
This paper addresses complex challenges in histopathological image analysis through three key contributions. Firstly, it introduces a fast patch selection method, FPS, for whole-sl…
When is a Foundation Model a Foundation Model
Saghir Alfasly, Peyman Nejat, Sobhan Hemati +13
Recently, several studies have reported on the fine-tuning of foundation models for image-text modeling in the field of medicine, utilizing images from online data sources such as…