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

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

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.CV2025

Enhancing Sentiment Analysis through Multimodal Fusion: A BERT-DINOv2 Approach

Taoxu Zhao, Meisi Li, Kehao Chen +6

Multimodal sentiment analysis enhances conventional sentiment analysis, which traditionally relies solely on text, by incorporating information from different modalities such as im…

cs.MM2025

Visual and Text Prompt Segmentation: A Novel Multi-Model Framework for Remote Sensing

Xing Zi, Kairui Jin, Xian Tao +4

Pixel-level segmentation is essential in remote sensing, where foundational vision models like CLIP and Segment Anything Model(SAM) have demonstrated significant capabilities in ze…