5 papers
From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathology
Basit Alawode, Moshira Ali Abdalla, Dwarikanath Mahapatra +3
Vision Transformers (ViTs) and their hierarchical variants have achieved strong performance in Computational Pathology (CPath). However, most are pre-trained on single-resolution W…
MLLM-HWSI: A Multimodal Large Language Model for Hierarchical Whole Slide Image Understanding
Basit Alawode, Arif Mahmood, Muaz Khalifa Al-Radi +6
Whole Slide Images (WSIs) exhibit hierarchical structure, where diagnostic information emerges from cellular morphology, regional tissue organization, and global context. Existing…
Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation
Shahad Albastaki, Anabia Sohail, Iyyakutti Iyappan Ganapathi +6
In Computational Pathology (CPath), the introduction of Vision-Language Models (VLMs) has opened new avenues for research, focusing primarily on aligning image-text pairs at a sing…
AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis
Basit Alawode, Iyyakutti Iyappan Ganapathi, Sajid Javed +3
The preservation of aquatic biodiversity is critical in mitigating the effects of climate change. Aquatic scene understanding plays a pivotal role in aiding marine scientists in th…
Transformer-Based Wireless Capsule Endoscopy Bleeding Tissue Detection and Classification
Basit Alawode, Shibani Hamza, Adarsh Ghimire +1
Informed by the success of the transformer model in various computer vision tasks, we design an end-to-end trainable model for the automatic detection and classification of bleedin…