most citedVision Transformers with Autoencoders and Explainable AI for Cancer Patient Risk Stratification Using Whole Slide Imaging

1 citations · 1 across the 7 of their papers we have counts for

collaborators

7 papers

cs.DC2025

Simplified Swarm Learning Framework for Robust and Scalable Diagnostic Services in Cancer Histopathology

Yanjie Wu, Yuhao Ji, Saiho Lee +3

The complexities of healthcare data, including privacy concerns, imbalanced datasets, and interoperability issues, necessitate innovative machine learning solutions. Swarm Learning…

cs.CV2025

Detecting and Understanding Hateful Contents in Memes Through Captioning and Visual Question-Answering

Ali Anaissi, Junaid Akram, Kunal Chaturvedi +1

Memes are widely used for humor and cultural commentary, but they are increasingly exploited to spread hateful content. Due to their multimodal nature, hateful memes often evade tr…

cs.CV2025

DualPrompt-MedCap: A Dual-Prompt Enhanced Approach for Medical Image Captioning

Yining Zhao, Ali Braytee, Mukesh Prasad

Medical image captioning via vision-language models has shown promising potential for clinical diagnosis assistance. However, generating contextually relevant descriptions with acc…

cs.CV2025

AeroLite: Tag-Guided Lightweight Generation of Aerial Image Captions

Xing Zi, Tengjun Ni, Xianjing Fan +4

Accurate and automated captioning of aerial imagery is crucial for applications like environmental monitoring, urban planning, and disaster management. However, this task remains c…

eess.IV20251 cited

Vision Transformers with Autoencoders and Explainable AI for Cancer Patient Risk Stratification Using Whole Slide Imaging

Ahmad Hussein, Mukesh Prasad, Ali Anaissi +1

Cancer remains one of the leading causes of mortality worldwide, necessitating accurate diagnosis and prognosis. Whole Slide Imaging (WSI) has become an integral part of clinical w…

cs.LG2025

FedSAF: A Federated Learning Framework for Enhanced Gastric Cancer Detection and Privacy Preservation

Yuxin Miao, Xinyuan Yang, Hongda Fan +6

Gastric cancer is one of the most commonly diagnosed cancers and has a high mortality rate. Due to limited medical resources, developing machine learning models for gastric cancer…