Parameter-efficient deep-learning-based model for segmentation with radiomic feature extraction

Daniel Sleiman; Navchetan Awasthi

doi:10.1117/1.jmi.13.2.024502

April 15, 2026Journal of Medical ImagingOpen Access

Parameter-efficient deep-learning-based model for segmentation with radiomic feature extraction

View Full Paper

Ask AI

Bookmark

Share

Key Points

The research aims to evaluate the effectiveness of parameter-efficient deep learning models for tumor segmentation in DCE-MRI images.
Developed a deep learning model focusing on parameter efficiency for image segmentation.
Utilized DCE-MRI imaging for tumor analysis.
Implemented radiomic feature extraction during the segmentation process.
Achieved competitive performance in tumor segmentation compared to more complex models.
Showed improvements in processing efficiency without significant loss in accuracy.

Implication

Demonstrates that parameter-efficient models achieve competitive performance in DCE-MRI tumor segmentation, implying promising advancements in imaging techniques.

Abstract

Our results demonstrate that parameter-efficient models can achieve competitive performance in DCE-MRI tumor segmentation.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Sleiman et al. (2026) studied this question.

synapsesocial.com/papers/69e470a4010ef96374d8d7f6 https://doi.org/10.1117/1.jmi.13.2.024502

View Full Paper

Ask AI

Bookmark

Share

Parameter-efficient deep-learning-based model for segmentation with radiomic feature extraction

Key Points

Implication

Abstract

Discussion

Cite This Study

Parameter-efficient deep-learning-based model for segmentation with radiomic feature extraction

Key Points

Implication

Abstract

Discussion

Cite This Study

Keep reading

More related papers

More from Journal of Medical Imaging

Keep reading

More related papers

More from Journal of Medical Imaging