Implantable Adaptive Cells: differentiable architecture search to improve the performance of any trained U-shaped network

• URL: http://arxiv.org/abs/2405.03420v1
• Date: Mon, 6 May 2024 12:40:15 GMT
• Title: Implantable Adaptive Cells: differentiable architecture search to improve the performance of any trained U-shaped network
• Authors: Emil Benedykciuk, Marcin Denkowski, Grzegorz Wójcik,
• Abstract summary: This paper introduces a novel approach to enhance the performance of pre-trained neural networks in medical image segmentation. We present the concept of Implantable Adaptive Cell (IAC), small but powerful modules identified through Partially-Connected DARTS. Our strategy allows for the seamless integration of the IAC into the pre-existing architecture, thereby enhancing its performance without necessitating a complete retraining from scratch.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper introduces a novel approach to enhance the performance of pre-trained neural networks in medical image segmentation using Neural Architecture Search (NAS) methods, specifically Differentiable Architecture Search (DARTS). We present the concept of Implantable Adaptive Cell (IAC), small but powerful modules identified through Partially-Connected DARTS, designed to be injected into the skip connections of an existing and already trained U-shaped model. Our strategy allows for the seamless integration of the IAC into the pre-existing architecture, thereby enhancing its performance without necessitating a complete retraining from scratch. The empirical studies, focusing on medical image segmentation tasks, demonstrate the efficacy of this method. The integration of specialized IAC cells into various configurations of the U-Net model increases segmentation accuracy by almost 2\% points on average for the validation dataset and over 3\% points for the training dataset. The findings of this study not only offer a cost-effective alternative to the complete overhaul of complex models for performance upgrades but also indicate the potential applicability of our method to other architectures and problem domains.

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