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Tumor segmentation in medical imaging is critical for diagnosis, treatment planning, and prognosis, yet remains challenging due to limited annotated data, tumor heterogeneity, and modality-specific complexities in CT, MRI, and histopathology. Although the Segment Anything Model (SAM) shows promise as a zero-shot learner, it struggles with irregular tumor boundaries and domain-specific variations. We introduce the Adaptive Unified Segmentation Anything Model (AUSAM). This novel framework extends SAM’s capabilities for multi-modal tumor segmentation by integrating an intelligent prompt module, dynamic sampling, and stage-based thresholding. Specifically, clustering-based prompt learning (DBSCAN for CT/MRI and K-means for histopathology) adaptively allocates prompts to capture challenging tumor regions, while entropy-guided sampling and dynamic thresholding systematically reduce annotation requirements and computational overhead. Validated on diverse benchmarks—LiTS (CT), FLARE 2023 (CT/MRI), ORCA, and OCDC (histopathology)—AUSAM achieves state-of-the-art Dice Similarity Coefficients (DSC) of 94.25%, 91.84%, 87.59%, and 91.84%, respectively, with significantly reduced data usage. As the first framework to adapt SAM for multi-modal tumor segmentation, AUSAM sets a new standard for precision, scalability, and efficiency. It is offered in two variants: AUSAM-Lite for resource-constrained environments and AUSAM-Max for maximum segmentation accuracy, thereby advancing medical imaging and clinical decision-making. 

The way media portray public health problems influences the public’s perception of problems and related solutions. Social media allows users to engage with news and to collectively construct meaning. This paper examined news in comparison to user-generated content related to opioids to understand the role of second-level agenda-setting in public health. We analyzed 162,760 tweets about the opioid crisis, and compared the main topics and their sentiments with 2998 opioid stories from The New York Times online. Evidence from this study suggests that second-level agenda setting on social media is different from the news; public communication about opioids on X/Twitter highlights attributes that are different from those highlighted in the news. The findings suggest that public health communication should strategically utilize social media data, including obtaining consumer insight from personal tweets, listening to diverse views and warning signs from issue tweets, and tuning in to the media for policy trends.