Authors - Joao Paulo Sousa, Tiago Lopes, Tatiana Ferreira, Tatiana Batista, Pedro Malheiro, Joao Vitorino, Barbara Barroso, Carlos Costa Abstract - Medical hyperspectral imaging (MHSI) represents a burgeoning paradigm in diagnostic visualization, capable of capturing contiguous spectral signatures across hundreds of narrow wavelengths to delineate pathological structures invisible to the human eye. Despite its diagnostic richness, the advancement of deep learning models in the MHSI domain is severely constrained by two primary challenges: the extreme scarcity of high-quality, pixel-level annotated datasets and the overwhelming data redundancy inherent in high-dimensional hypercubes. Traditional self-supervised methods, particularly masked image modeling, often fail to prioritize discriminative tissue signatures, while domain-agnostic transfer learning from natural images proves inappropriate due to structural and feature-level incongruities. This paper introduces a novel high-quality research methodology: Reinforced Spatio-Spectral In-Context Learning (RSS-ICL). This framework integrates an asynchronous advantage actor-critic (A3C) reinforcement learning agent with visual in-context learning (ICL). The proposed model employs the RL agent to dynamically learn adaptive masking strategies that prioritize high-entropy, "hardto- reconstruct" spatio-spectral voxels, thereby forcing the backbone architecture to capture intricate biochemical signatures during pre-training. By reformulating segmentation as a supportquery inpainting task, RSS-ICL facilitates universal medical segmentation, allowing the model to adapt to novel clinical tasks and unseen tissue types in a zero-shot or one-shot manner. Theoretical arguments suggest that this synergistic approach effectively bridges the gap between low-level signal recovery and high-level semantic understanding in hyperspectral analysis. Through rigorous methodological development and empirical support from existing selfsupervised benchmarks, this paper outlines a path for accelerating the deployment of interpretable, annotation-efficient clinical AI.
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