Bone Tumor Grading Using Radiomic-Guided Feature Modulation: In A Compact Deep Learning Model

Accurate grading of tumors in the bones is very important in delivering treatment methods and also giving a prognosis to the patient. Conventional classification techniques usually cannot cope with the heterogeneity of the tumor picture and the absence of annotated data. This research work presents a new lightweight deep learning architecture called Radiomic-Modulated Deep Network (RMD-Net) that is aimed at improving the performance of tumor grading based on the combination of radiomic and deep visual image representations. The model utilizes radiomic descriptors in the form of shape, intensity, and texture measurements of segmented tumor regions, and is trained to modulate deep feature activations produced by a shallow convolutional or transformer-based backbone dynamically as symbols of target changes. The resulting radiomic-guided modulation will provide interpretability of the features and better Modularity of the classes because the learning process encapsulates clinically useful properties. A vast amount of experiments on a curated set of CT and MRI scans show that RMD-Net is better at multi-class bone tumor grading tasks, making it more accurate and generalizing with much less parameters. The suggested framework is an effective, interpretable, and clinically flexible solution to assist radiologists in the non-invasive measures of the severity of bone tumors.