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20232025
most citedVision-Language Models for Medical Report Generation and Visual Question Answering: A Review

214 citations · 379 across the 14 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

eess.IV2024★ 2 cited

Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review

Nikolas Koutsoubis, Asim Waqas, Yasin Yilmaz +3

Artificial Intelligence (AI) has demonstrated significant potential in automating various medical imaging tasks, which could soon become routine in clinical practice for disease di…

cs.LG2024★ 2 cited

Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation

Nikolas Koutsoubis, Yasin Yilmaz, Ravi P. Ramachandran +2

Machine learning (ML) and Artificial Intelligence (AI) have fueled remarkable advancements, particularly in healthcare. Within medical imaging, ML models hold the promise of improv…

q-bio.CB2024★ 2 cited

Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes

Asim Waqas, Aakash Tripathi, Paul Stewart +3

Cancer clinics capture disease data at various scales, from genetic to organ level. Current bioinformatic methods struggle to handle the heterogeneous nature of this data, especial…

cs.LG2024★ 2 cited

Self-Normalizing Foundation Model for Enhanced Multi-Omics Data Analysis in Oncology

Asim Waqas, Aakash Tripathi, Sabeen Ahmed +6

Multi-omics research has enhanced our understanding of cancer heterogeneity and progression. Investigating molecular data through multi-omics approaches is crucial for unraveling t…

cs.LG2024★ 3 cited

HoneyBee: A Scalable Modular Framework for Creating Multimodal Oncology Datasets with Foundational Embedding Models

Aakash Tripathi, Asim Waqas, Matthew B. Schabath +2

HONeYBEE (Harmonized ONcologY Biomedical Embedding Encoder) is an open-source framework that integrates multimodal biomedical data for oncology applications. It processes clinical…

cs.CV2024★ 214 cited

Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review

Iryna Hartsock, Ghulam Rasool

Medical vision-language models (VLMs) combine computer vision (CV) and natural language processing (NLP) to analyze visual and textual medical data. Our paper reviews recent advanc…