Integrating Local Detail and Global Context: A Dual-Input Multi-Task Learning Framework for Bone Tumor Diagnosis

arXiv:2609.28732 · eess.IV, cs.CV · Submitted 2026-09-23 · Read on arXiv

eess.IV, cs.CV

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Accuracy — 29× in this paper · explained in 1 episode
• AUC — 14× in this paper · explained in 2 episodes
• Attention Mechanism — 7× in this paper · explained in 2 episodes
• Regularization — 7× in this paper · explained in 3 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Loss Function — 5× in this paper · explained in 2 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Dice Similarity Coefficient — 3× in this paper
• Generalizability — 3× in this paper · explained in 6 episodes
• Gradient Flow — 3× in this paper · explained in 2 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Segmentation Masks — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Bone Tumor X-ray Radiograph Dataset — 2× in this paper
• Calibration — 2× in this paper · explained in 7 episodes
• Computer Vision and Pattern Recognition — 2× in this paper
• Convolutional Block Attention Module — 2× in this paper
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Transfer Learning — 2× in this paper · explained in 10 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Contextual Understanding — 1× in this paper · explained in 2 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Late Fusion — 1× in this paper · explained in 2 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• YOLO Evolution: A Comprehensive Benchmark and Architectural Review of YOLOv12, YOLO11, and Their Previous Versions
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Attention Is All You Need
• Squeeze-and-Excitation Networks
• Attention U-Net: Learning Where to Look for the Pancreas
• Going Deeper with Convolutions
• Training Deeper Convolutional Networks with Deep Supervision

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