FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation
Abstract
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.}
Authors 6
-
Iran University of Science and Technology
Affiliation as printed
School of Electrical Engineering , Iran University of Science and Technology , Iran
-
Affiliation as printed
Faculty of Informatics and Data Science , University of Regensburg , Germany
-
Affiliation as printed
Faculty of Electrical Engineering and Information Technology , RWTH Aachen University , Germany
-
Northwestern University · Northwestern University · Northwestern Medicine
Affiliation as printed
Department of Radiology , Northwestern University , Chicago , IL , USA
-
Northwestern University · Northwestern University · Northwestern Medicine
Affiliation as printed
Department of Radiology , Northwestern University , Chicago , IL , USA
-
Fraunhofer Institute for Digital Medicine · University of Regensburg
Affiliation as printed
Faculty of Informatics and Data Science , University of Regensburg , Germany
Fraunhofer Institute for Digital Medicine MEVIS , Germany
Cited by 0 stored of 0
No patents citing this paper on Lens.org (checked 2026-10-06).