Semantic segmentation, a crucial task in computer vision, often relies on labor-intensive and costly annotated datasets for training. In response to this challenge, we introduce FuseNet, a dual-stream framework for self-supervised semantic segmentation that eliminates the need for manual annotation. FuseNet leverages the shared semantic dependencies between the original and augmented images to create a clustering space, effectively assigning pixels to semantically related clusters, and ultimately generating the segmentation map. Additionally, FuseNet incorporates a cross-modal fusion technique that extends the principles of CLIP by replacing textual data with augmented images. This approach enables the model to learn complex visual representations, enhancing robustness against variations similar to CLIP's text invariance. To further improve edge alignment and spatial consistency between neighboring pixels, we introduce an edge refinement loss. This loss function considers edge information to enhance spatial coherence, facilitating the grouping of nearby pixels with similar visual features. Extensive experiments on skin lesion and lung segmentation datasets demonstrate the effectiveness of our method. \href{https://github.com/xmindflow/FuseNet}{Codebase.}

FuseNet是一种双流框架，用于自我监督的语义分割，通过利用原始图像和增强图像之间的共享语义依赖来创建聚类空间，从而消除了手动标注的需求。它还结合了交叉模态融合技术和边缘细化损失函数，通过增强空间一致性和边缘对齐来提高语义分割的效果。

FuseNet：自监督的医学图像分割双通路网络