AM-SAM: Automated Prompting and Mask Calibration for Segment Anything Model
Segment Anything Model (SAM) has gained significant recognition in the field of semantic
segmentation due to its versatile capabilities and impressive performance. Despite its …
segmentation due to its versatile capabilities and impressive performance. Despite its …
How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model
Automated segmentation is a fundamental medical image analysis task, which enjoys
significant advances due to the advent of deep learning. While foundation models have …
significant advances due to the advent of deep learning. While foundation models have …
Transformer Architecture Search for Improving Out-of-Domain Generalization in Machine Translation
Interest in automatically searching for Transformer neural architectures for machine
translation (MT) has been increasing. Current methods show promising results in in-domain …
translation (MT) has been increasing. Current methods show promising results in in-domain …
SAM-UNet: a new model for medical image segmentation
S Temmar - dspace.univ-ouargla.dz
Early detection and assessment of polyps are crucial in the prevention and treatment of
colorectal cancer (CRC). Accurate polyp segmentation assists clinicians by precisely …
colorectal cancer (CRC). Accurate polyp segmentation assists clinicians by precisely …
Improving SAM model for medical image segmentation
T Rezzag Bedida, A Hammouya - dspace.univ-ouargla.dz
Early detection of polyps in the colon is crucial for preventing colorectal cancer, the second
leading cause of cancer-related deaths globally. However, accurate identification of polyps …
leading cause of cancer-related deaths globally. However, accurate identification of polyps …