Fusenet: Self-Supervised Dual-Path Network For Medical Image Segmentation

IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1–5

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. GitHub.

Authors 6

1. Amirhossein Kazerouni

   Iran University of Science and Technology

   Affiliation as printed

   Iran University of Science and Technology,School of Electrical Engineering,Iran

2. Sanaz Karimijafarbigloo

   University of Regensburg

   Affiliation as printed

   University of Regensburg,Faculty of Informatics and Data Science,Germany

3. Reza Azad Aachen Faculty of Electrical Engineering and Information Technology

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Faculty of Electrical Engineering and Information Technology,Germany

4. Yury Velichko

   Northwestern University

   Affiliation as printed

   Northwestern University,Department of Radiology,Chicago,IL,USA

5. Ulaş Bağcı

   Northwestern University

   Affiliation as printed

   Northwestern University,Department of Radiology,Chicago,IL,USA

6. Dorit Merhof

   University of Regensburg

   Affiliation as printed

   University of Regensburg,Faculty of Informatics and Data Science,Germany