5 papers
Retrieving Patient-Specific Radiomic Feature Sets for Transparent Knee MRI Assessment
Yaxi Chen, Simin Ni, Jingjing Zhang +7
Classical radiomic features are designed to quantify image appearance and intensity patterns. Compared with end-to-end deep learning (DL) models trained for disease classification,…
Interpretability and Individuality in Knee MRI: Patient-Specific Radiomic Fingerprint with Reconstructed Healthy Personas
Yaxi Chen, Simin Ni, Shuai Li +6
For automated assessment of knee MRI scans, both accuracy and interpretability are essential for clinical use and adoption. Traditional radiomics rely on predefined features chosen…
Radiomic fingerprints for knee MR images assessment
Yaxi Chen, Simin Ni, Shaheer U. Saeed +5
Accurate interpretation of knee MRI scans relies on expert clinical judgment, often with high variability and limited scalability. Existing radiomic approaches use a fixed set of r…
Patient-specific radiomic feature selection with reconstructed healthy persona of knee MR images
Yaxi Chen, Simin Ni, Aleksandra Ivanova +5
Classical radiomic features have been designed to describe image appearance and intensity patterns. These features are directly interpretable and readily understood by radiologists…
Segmentation by registration-enabled SAM prompt engineering using five reference images
Yaxi Chen, Aleksandra Ivanova, Shaheer U. Saeed +4
The recently proposed Segment Anything Model (SAM) is a general tool for image segmentation, but it requires additional adaptation and careful fine-tuning for medical image segment…