YOLO11 and RF-DETR showed complementary strengths in sensitivity and precision, respectively, and should be regarded as exploratory support tools requiring further training, patient-level validation, and external multicenter evaluation before clinical implementation.
The edge-deployed two-stage deep learning desktop system may serve as an objective adjunct to conventional ultrasound interpretation, potentially assisting clinicians during preoperative evaluation.
ST-USNet serves as an effective and practical decision support system for superficial STMs classification in clinical oncology, though multi-center prospective validation and continuous model updating are required before clinical deployment.
Meng-Jie Wu, Yu-Lu Wu, Ao Li et al.· Academic Radiology· 0 citations
DL enabled accurate CECT-based identification of AP in this retrospective multicenter cohort, with performance maintained in an independent external dataset, and showed promising performance for CECT-based acute pancreatitis detection.
Oleksandra Seidel, M. Theis, Sebastian Nowak et al.· European Radiology Experimen...· 0 citations
LSTAC provides an efficient and accurate solution for automated CEUS analysis, reducing manual workload and improving consistency in thyroid nodule assessment, with potential for clinical application.
Aoxiang Yang, Liuyue Li, Ruifan He et al.· Artif. Intell. Medicine· 0 citations
A hybrid architecture in which ResNet50 is employed for localized spatial feature extraction, while Vision Transformer enables global contextual learning to automatically classify kidney tumors into multiple classes is proposed.
Hivi Kamal, W. M. Abduallah· passer of basic and applied...· 0 citations
The findings show that early liver cancer screening is a good fit for a lightweight, scalable classification pipeline, and future research should focus on improving small lesion localisation.
Megha J., Gopalakrishna Madigondanahalli Thimmaiah, V. B. Gowda· International journal of com...· 0 citations
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