Keywords
vision-language modeling
breast cancer
Abstract
Trustworthy oncology vision-language modeling requires the alignment of imaging evidence, molecular mechanisms, immune microenvironment information, and clinical risk factors. This topic integrates breast cancer DCE-MRI segmentation, low-dose medical imaging, immune scoring, gastric cancer risk analysis, cervical cancer prognosis, metalloptosis-based tumor therapy, and multimodal visual representation learning. Instance-aware super-resolution and forensic vision-language compression support efficient and high-quality visual evidence processing, while hyperspectral-LiDAR fusion contributes broader multimodal alignment techniques. Knowledge graph-based epilepsy reasoning provides a methodological reference for evidence-intensive medical reasoning, especially when multiple evidence types must be traced and interpreted. The literature structure supports oncology AI systems that connect image-level findings with molecular and clinical evidence in a transparent manner.
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