8 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…
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…
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…
Detection of Breast Cancer Lumpectomy Margin with SAM-incorporated Forward-Forward Contrastive Learning
Tyler Ward, Xiaoqin Wang, Braxton McFarland +6
Complete removal of cancer tumors with a negative specimen margin during lumpectomy is essential in reducing breast cancer recurrence. However, 2D specimen radiography (SR), the cu…
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…
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…
Annotation-Efficient Task Guidance for Medical Segment Anything
Tyler Ward, Abdullah-Al-Zubaer Imran
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…