From the 2 of 8 linked papers with an AI index.
8 papers
SARFA: Segment Anything with Radiomic Feature Alignment
Tyler Ward, Abdullah Imran
The paper introduces SARFA, a framework that improves ambiguous medical image segmentation by generating multiple candidate masks and aligning them with radiomic features using Fré…
Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training?
Nusrat Munia, Tyler Ward, Nishat Nayla +2
The paper empirically compares pretraining‑finetuning and joint training of self‑supervised and supervised objectives across multiple SSL methods and vision tasks, showing that joi…
Domain and Task-Focused Example Selection for Data-Efficient Contrastive Medical Image Segmentation
Tyler Ward, Aaron Moseley, Abdullah-Al-Zubaer Imran
Segmentation is one of the most important tasks in the medical imaging pipeline as it influences a number of image-based decisions. To be effective, fully supervised segmentation a…
Autoadaptive Medical Segment Anything Model
Tyler Ward, Meredith K. Owen, O'Kira Coleman +2
Medical image segmentation is a key task in the imaging workflow, influencing many image-based decisions. Traditional, fully-supervised segmentation models rely on large amounts of…
Improving Brain Disorder Diagnosis with Advanced Brain Function Representation and Kolmogorov-Arnold Networks
Tyler Ward, Abdullah-Al-Zubaer Imran
Quantifying functional connectivity (FC), a vital metric for the diagnosis of various brain disorders, traditionally relies on the use of a pre-defined brain atlas. However, using…
A Probabilistic Segment Anything Model for Ambiguity-Aware Medical Image Segmentation
Tyler Ward, Abdullah Imran
Recent advances in promptable segmentation, such as the Segment Anything Model (SAM), have enabled flexible, high-quality mask generation across a wide range of visual domains. How…