most citedSeeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities

20 citations · 53 across the 8 of their papers we have counts for

collaborators

8 papers

eess.IV2024

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…

eess.IV2024★ 10 cited

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…

cs.CV2023★ 17 cited

Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review

Ricardo Gonzalez, Peyman Nejat, Ashirbani Saha +3

Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important evidence of t…

cs.CV2023★ 20 cited

Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities

Ricardo Gonzalez, Ashirbani Saha, Clinton J. V. Campbell +3

This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Syst…

cs.CV2023

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…

cs.CV2023

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…

eess.IV2023

Creating an Atlas of Normal Tissue for Pruning WSI Patching Through Anomaly Detection

Peyman Nejat, Areej Alsaafin, Ghazal Alabtah +7

Patching gigapixel whole slide images (WSIs) is an important task in computational pathology. Some methods have been proposed to select a subset of patches as WSI representation fo…

cs.IR2023★ 6 cited

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…