RadiomicNet: A Hybrid Radiomics-Guided Lightweight Architecture for Interpretable Medical Image Segmentation
arXiv:2607.02185v1 Announce Type: cross Abstract: Deep learning has achieved remarkable performance in medical image segmentation, yet it suffers from critical limitations: mathematical intractability, substantial parameter requirements, and lack of clinical interpretability. We propose RadiomicNet, a novel two-stream hybrid architecture that enhances standard deep learning by integrating handcrafted radiomics features directly into the segmentation learning process. The key contribution is the Radiomics Attention Gate (RAG), which leverages Gray-Level Co-occurrence Matrix (GLCM) and Local Bin
The increasing complexity of AI models in medical imaging necessitates new approaches to address interpretability, parameter efficiency, and clinical applicability.
This development offers a pathway to more trustworthy and efficient AI in critical applications like medical diagnosis, potentially accelerating adoption and regulatory approval.
Medical AI models may become more transparent and resource-efficient, leading to broader accessibility and improved integration into clinical workflows.
- · Medical diagnostic companies
- · Healthcare providers
- · AI hardware manufacturers
- · Medical researchers
- · AI models lacking interpretability
Improved and more widespread use of AI in medical imaging interpretation.
Reduced healthcare costs and improved patient outcomes through earlier and more accurate diagnoses.
New regulatory frameworks specifically designed for interpretable and hybrid AI models in medicine.
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Read at arXiv cs.AI