RT-GAN: Recurrent Temporal GAN for Adding Lightweight Temporal Consistency to Frame-Based Domain Translation Approaches

• URL: http://arxiv.org/abs/2310.00868v1
• Date: Mon, 2 Oct 2023 03:13:26 GMT
• Title: RT-GAN: Recurrent Temporal GAN for Adding Lightweight Temporal Consistency to Frame-Based Domain Translation Approaches
• Authors: Shawn Mathew, Saad Nadeem, Alvin C. Goh, and Arie Kaufman
• Abstract summary: We present a lightweight solution with a tunable temporal parameter, RT-GAN, for adding temporal consistency to individual frame-based approaches. We demonstrate the effectiveness of our approach on two challenging use cases in colonoscopy.
• Score: 3.7873597471903944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While developing new unsupervised domain translation methods for endoscopy videos, it is typical to start with approaches that initially work for individual frames without temporal consistency. Once an individual-frame model has been finalized, additional contiguous frames are added with a modified deep learning architecture to train a new model for temporal consistency. This transition to temporally-consistent deep learning models, however, requires significantly more computational and memory resources for training. In this paper, we present a lightweight solution with a tunable temporal parameter, RT-GAN (Recurrent Temporal GAN), for adding temporal consistency to individual frame-based approaches that reduces training requirements by a factor of 5. We demonstrate the effectiveness of our approach on two challenging use cases in colonoscopy: haustral fold segmentation (indicative of missed surface) and realistic colonoscopy simulator video generation. The datasets, accompanying code, and pretrained models will be made available at \url{https://github.com/nadeemlab/CEP}.

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