6 papers
Advancing Multimodal Fusion on Heterogeneous Medical Data with Hybrid Geometry Attention
Joy Dhar, Manish Kumar Pandey, Nayyar Zaidi +4
Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. Ho…
Certified vs. Empirical Adversarial Robust-ness via Hybrid Convolutions with Attention Stochasticity
Joy Dhar, Song Xia, Manish Kumar Pandey +5
We introduce Hybrid Convolutions with Attention Stochasticity (HyCAS), an adversarial defense that narrows the long-standing gap between provable robustness under L2 certificates a…
HyPCA-Net: Advancing Multimodal Fusion in Medical Image Analysis
J. Dhar, M. K. Pandey, D. Chakladar +4
Multimodal fusion frameworks, which integrate diverse medical imaging modalities (e.g., MRI, CT), have shown great potential in applications such as skin cancer detection, dementia…
Effective and Robust Multimodal Medical Image Analysis
Joy Dhar, Nayyar Zaidi, Maryam Haghighat
Multimodal Fusion Learning (MFL), leveraging disparate data from various imaging modalities (e.g., MRI, CT, SPECT), has shown great potential for addressing medical problems such a…
Intra-Class Probabilistic Embeddings for Uncertainty Estimation in Vision-Language Models
Zhenxiang Lin, Maryam Haghighat, Will Browne +1
Vision-language models (VLMs), such as CLIP, have gained popularity for their strong open vocabulary classification performance, but they are prone to assigning high confidence sco…
Multimodal Fusion Learning with Dual Attention for Medical Imaging
Joy Dhar, Nayyar Zaidi, Maryam Haghighat +4
Multimodal fusion learning has shown significant promise in classifying various diseases such as skin cancer and brain tumors. However, existing methods face three key limitations.…