17 citations · 33 across the 18 of their papers we have counts for
9 papers · 1 filter
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.…
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…
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…