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For University of Pittsburgh Department of Radiology in 2018-2020, the graph shows 22 visible PIs and 14 internal PI collaborations. The dominant subject mix is Medicine as the leading field (49% of slots across 20 PIs; 20 labels), Radiology, Nuclear Medicine and Imaging as the leading subfield (25% of slots across 15 PIs; 15 labels), and Medical Imaging Techniques and Applications as the leading topic (8% of slots across 5 PIs; 5 labels). The network looks reasonably well-rounded, with visible collaboration groups but not so much concentration that one field explains the whole department. The top weighted PIs are Shandong Wu (12.5 weighted works; AI in cancer detection, Radiomics and Machine Learning in Medical Imaging). The most visible ties are William E. Klunk and Beth E. Snitz (26 shared works, weight 8.9). The standout breakdown groups are group 1 with 6 PIs, 5 internal connections, weight 11.9, around Radiology, Nuclear Medicine and Imaging, Cellular and Molecular Neuroscience, Psychiatry and Mental health, led by Michel Modo, William E. Klunk, Beth E. Snitz.