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arXiv Computer Vision By S. M. Nasif Uddin, Rusab Sarmun, Muhammad E. H. Chowdhury, Adam Mushtak, Israa Al-Hashimi, Sohaib Bassam Zoghoul

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

Read the original on arXiv Computer Vision →

The paper introduces a dual‑input, multi‑task learning framework that jointly segments and classifies bone tumors by applying bidirectional cross‑modal attention between a lesion crop and the full radiograph. Using a YOLO‑based detector and a dual‑stream DenseNet121 architecture, the model fuses fine‑grained lesion detail with global anatomical context through a novel cross‑modal attention fusion strategy and hierarchical multi‑scale feature fusion. On the multi‑institutional Bone Tumor X‑ray Radiograph Dataset, the approach outperforms single‑input baselines, achieving a Dice coefficient of 0.896 and a macro‑averaged F1‑score of 0.928, with an AUC of 0.999 for malignant osteosarcoma.

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