Mamba-Enhanced Vision-Language Framework for Intelligent Polyp Detection and Clinical Endoscopy Report Generation
Keywords:
polyp detection; endoscopy report generation; vision-language models; state space models; Mamba architectures; medical image segmentation; clinical decision support; deployment governanceAbstract
Colorectal cancer prevention depends heavily on the reliable identification and removal of precancerous polyps during endoscopic examination. Although deep learning has improved polyp detection and segmentation, many clinical systems remain narrowly task-oriented and do not address the interpretive documentation workload that follows a procedure. This paper presents a system-level analysis of a Mamba-enhanced vision-language framework that unifies intelligent polyp detection with structured clinical endoscopy report generation. The framework combines selective state space modeling for efficient long-range visual encoding with a vision-language alignment module and a constrained language decoder. The design is discussed from structural, infrastructural, and governance perspectives rather than as a purely algorithmic contribution. The analysis examines how linear-time sequence modeling can reduce memory burdens in long endoscopic video streams while preserving global context for polyp localization. It further considers how language generation can be coupled to visual evidence to produce preliminary reports that support rather than replace clinical judgment. Key trade-offs are addressed across detection sensitivity, false positive burden, language grounding, hallucination control, interoperability with electronic health records, and regulatory compliance. The paper also evaluates fairness risks arising from dataset composition and deployment heterogeneity, and it proposes governance mechanisms that may support safer translation into real clinical environments. The discussion emphasizes that the value of such a framework lies less in isolated benchmark performance than in its ability to operate within an accountable, auditable, and sustainable sociotechnical infrastructure.
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