17 citations · 33 across the 18 of their papers we have counts for
19 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…
CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval
Zahra Rahimi Afzal, Wataru Uegami, Saghir Alfasly +6
Digital pathology archives increasingly contain multiple whole-slide images (WSIs) per case, capturing spatially distinct tumor regions and reflecting intrinsic morphological heter…
Validation of Whole-Slide Foundation Models for Image Retrieval in TCGA Data
Tianhao Lei, Parsa Esmaeilkhani, Saghir Alfasly +5
Foundation models are reshaping computational histopathology, yet their value for whole-slide image retrieval relative to strong patch-based and supervised aggregation baselines re…
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…
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…
AlcLaM: Arabic Dialectal Language Model
Murtadha Ahmed, Saghir Alfasly, Bo Wen +3
Pre-trained Language Models (PLMs) are integral to many modern natural language processing (NLP) systems. Although multilingual models cover a wide range of languages, they often g…
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…
OSRE: Object-to-Spot Rotation Estimation for Bike Parking Assessment
Saghir Alfasly, Zaid Al-huda, Saifullah Bello +3
Current deep models provide remarkable object detection in terms of object classification and localization. However, estimating object rotation with respect to other visual objects…
Learnable Irrelevant Modality Dropout for Multimodal Action Recognition on Modality-Specific Annotated Videos
Saghir Alfasly, Jian Lu, Chen Xu +1
With the assumption that a video dataset is multimodality annotated in which auditory and visual modalities both are labeled or class-relevant, current multimodal methods apply mod…
Variational Representation Learning for Vehicle Re-Identification
Saghir Ahmed Saghir Alfasly, Yongjian Hu, Tiancai Liang +3
Vehicle Re-identification is attracting more and more attention in recent years. One of the most challenging problems is to learn an efficient representation for a vehicle from its…