2024/09/19 by Yılmaz Korkmaz, Vishal M. Patel, Korkmaz, Yilmaz +1
Computer Science · Neuroscience · #AI in cancer detection #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image Retrieval and Classification Techniques #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2409.12399
openalex publication_date 2024/09/19 · openalex created_date 2024/10/25 · openalex updated_date 2026/07/28
Unpaired image-to-image translation is a challenging task due to the absence of paired examples, which complicates learning the complex mappings between the distinct distributions of the source and target domains. One of the most commonly used approach for this task is CycleGAN which requires the training of a new pair of generator-discriminator networks for each domain pair. In this paper, we propose a new image-to-image translation framework named Image-to-Image-Generative-Adversarial-CLIP (I2I-Galip) where we utilize a pre-trained multi-model foundation model (i.e., CLIP) to mitigate the need of separate generator-discriminator pairs for each source-target mapping while achieving better and more efficient multi-domain translation. By utilizing the massive knowledge gathered during pre-training a foundation model, our approach makes use of a single lightweight generator network with ~13M parameters for the multi-domain image translation task. Comprehensive experiments on translation performance in public MRI and CT datasets show the superior performance of the proposed framework over the existing approaches. Code will be available (https://github.com/yilmazkorkmaz1/I2I-Galip).