While CNNs were long considered state of the art for image processing, the introduction of Transformer architectures has challenged this position. While achieving excellent results in image classification and segmentation, Transformers remain inherently reliant on large training datasets and remain computationally expensive. A newly introduced Transformer derivative named KV Transformer shows promising results in synthetic, NLP, and image classification tasks, while reducing complexity and memory usage. This is especially conducive to use cases where local inference is required, such as medical screening applications. We endeavoured to further evaluate the merit of KV Transformers on semantic segmentation tasks, specifically in the domain of medical imaging. By directly comparing traditional and KV variants of the same base architectures, we provide further insight into the practical tradeoffs of reduced model complexity. We observe a notable reduction in parameter count and multiply accumulate operations, while achieving similar performance from most of the KV variant models when directly compared to their QKV implementation.

本研究解决了变换器在语义分割任务中的计算复杂性和数据依赖性问题，提出了将关键值变换器（KV Transformer）应用于医学成像的创新方法。研究发现，KV 变换器在降低模型复杂性的同时，可以保持与传统 QKV 实现相似的性能，从而有望在医疗筛查等需要局部推理的应用场景中带来积极影响。

探讨关键值注意力在纯和混合变换器中的集成用于语义分割