10 citations · 10 across the 10 of their papers we have counts for
11 papers
WILSON - Multi-magnification composites as a foundation for patient-level pathology analysis and diagnostic text generation
Saghir Alfasly, Wataru Uegami, Sobhan Hemati +17
Pathologists examine tissue sections across multiple magnifications and slides, integrating morphological information to establish a diagnosis. In contrast, most pathology foundati…
Reducing Redundancy in Whole-Slide Image Patching for Scalable Indexing and Retrieval
Jialiang Geng, Ghazal Alabtah, Saghir Alfasly +2
The rapid growth of digital pathology has created an urgent need for efficient indexing and retrieval of whole slide images (WSIs). This need is intensified by emerging generative…
Retrieval-Guided Generation for Safer Histopathology Image Captioning
Md. Enamul Hoq, Wataru Uegami, Saghir Alfasly +6
Generative vision-language models can produce fluent medical image captions but remain prone to hallucination, over-specific diagnostic claims, and factual inconsistency-serious is…
Semantic and Visual Crop-Guided Diffusion Models for Heterogeneous Tissue Synthesis in Histopathology
Saghir Alfasly, Wataru Uegami, MD Enamul Hoq +2
Synthetic data generation in histopathology faces unique challenges: preserving tissue heterogeneity, capturing subtle morphological features, and scaling to unannotated datasets.…
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…
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…
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…
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…