3 citations · 9 across the 18 of their papers we have counts for
23 papers
SARFA: Segment Anything with Radiomic Feature Alignment
Tyler Ward, Abdullah Imran
The Segment Anything Model (SAM) has demonstrated strong generalizability across a variety of segmentation tasks. However, SAM often struggles in situations where the target to be…
Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training?
Nusrat Munia, Tyler Ward, Nishat Nayla +2
Self-supervision is a powerful technique for learning visual representations from unlabeled data. Existing techniques primarily adopt a two-stage approach for self-supervised learn…
Protecting and Preserving Protest Dynamics for Responsible Analysis
Cohen Archbold, Usman Hassan, Nazmus Sakib +2
Protest-related social media data are valuable for understanding collective action but inherently high-risk due to concerns surrounding surveillance, repression, and individual pri…
CAP-IQA: Context-Aware Prompt-Guided CT Image Quality Assessment
Kazi Ramisa Rifa, Jie Zhang, Abdullah Imran
Prompt-based methods, which encode medical priors through descriptive text, have been only minimally explored for CT Image Quality Assessment (IQA). While such prompts can embed pr…
ABFR-KAN: Kolmogorov-Arnold Networks for Functional Brain Analysis
Tyler Ward, Abdullah Imran
Functional connectivity (FC) analysis, a valuable tool for computer-aided brain disorder diagnosis, traditionally relies on atlas-based parcellation. However, issues relating to se…
Class-N-Diff: Classification-Induced Diffusion Model Can Make Fair Skin Cancer Diagnosis
Nusrat Munia, Abdullah Imran
Generative models, especially Diffusion Models, have demonstrated remarkable capability in generating high-quality synthetic data, including medical images. However, traditional cl…