RadiomicNet: A Hybrid Radiomics-Guided Lightweight Architecture for Interpretable Medical Image Segmentation
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 Binary Pattern (LBP) features to modulate skip-connec
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- PossiblePossibly related (embedding) · 52%NVIDIA/physicsnemo →
- PossiblePossibly related (embedding) · 47%DIAGNijmegen/rse-grand-challenge →
- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
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- LinkedLinked via arxiv author · 85%Mohammad Amanour Rahman →
“RadiomicNet: A Hybrid Radiomics-Guided Lightweight Architecture for Interpretable Medical Image Segmentation”
- PossiblePossibly related (embedding) · 51%mlmed/torchxrayvision →
- PossiblePossibly related (embedding) · 51%DeepTrackAI/DeepTrack2 →
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