17 citations · 33 across the 18 of their papers we have counts for
6 papers · 1 filter
Overfitting in Histopathology Model Training: The Need for Customized Architectures
Saghir Alfasly, Ghazal Alabtah, H. R. Tizhoosh
This study investigates the critical problem of overfitting in deep learning models applied to histopathology image analysis. We show that simply adopting and fine-tuning large-sca…
Aggregation Schemes for Single-Vector WSI Representation Learning in Digital Pathology
Sobhan Hemati, Ghazal Alabtah, Saghir Alfasly +1
A crucial step to efficiently integrate Whole Slide Images (WSIs) in computational pathology is assigning a single high-quality feature vector, i.e., one embedding, to each WSI. Wi…
Zero-Shot Whole Slide Image Retrieval in Histopathology Using Embeddings of Foundation Models
Saghir Alfasly, Ghazal Alabtah, Sobhan Hemati +2
We have tested recently published foundation models for histopathology for image retrieval. We report macro average of F1 score for top-1 retrieval, majority of top-3 retrievals, a…
Multimodal Learning for Scalable Representation of High-Dimensional Medical Data
Areej Alsaafin, Abubakr Shafique, Saghir Alfasly +2
Integrating artificial intelligence (AI) with healthcare data is rapidly transforming medical diagnostics and driving progress toward precision medicine. However, effectively lever…
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…