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arXiv:2606. 15457v1 Announce Type: cross Abstract: 3D FLAIR MRI is widely recommended as one of the standard MRI sequences for brain imaging in multiple sclerosis (MS), but publicly available MS datasets remain relatively small and vary across scanners, acquisition protocols, and lesion patterns.
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.
arXiv:2509. 21913v2 Announce Type: replace-cross Abstract: Background: Cone-beam computed tomography CBCT is a commonly used modality for image guided radiotherapy.
arXiv:2606. 24313v1 Announce Type: new Abstract: AI-driven image-to-image synthesis is rapidly advancing, with growing applications in medical imaging.
arXiv:2607. 07401v1 Announce Type: cross Abstract: While whole-body multimodal medical imaging scanners have been increasingly recognized for more effective medical applications, the excessive long acquisition time in PET-MR scanning is a major obstacle in more efficient clinical practice.
AI-driven image-to-image synthesis is rapidly advancing, with growing applications in medical imaging. Multi-modal image analysis plays a crucial role in optimizing examination quality, yet acquiring multiple imaging modalities in clinical settings remains resource-intensive and time-consuming, especially for 3D imaging.