Adaptive Smooth Activation Function for Improved Organ Segmentation and Disease Diagnosis

The design of activation functions constitutes a cornerstone for deep learning (DL) applications, exerting a profound influence on the performance and capabilities of neural networks. This influence stems from their ability to introduce non-linearity into the network architecture. By doing so, activation functions empower the network to learn and model intricate data patterns and relationships, surpassing the limitations of linear models. In this study, we propose a new activation function, called Adaptive Smooth Activation Unit (ASAU), tailored for optimized gradient propagation, thereby enhancing the proficiency of deep networks in medical image analysis. We apply this new activation function to two important and commonly used general tasks in medical image analysis: automatic disease diagnosis and organ segmentation in CT and MRI scans. Our rigorous evaluation on the RadImageNet abdominal/pelvis (CT and MRI) demonstrates that our ASAU-integrated classification frameworks achieve a substantial improvement of 4.80% over ReLU based frameworks in classification accuracy for disease detection. Also, the proposed framework on Liver Tumor Segmentation (LiTS) 2017 Benchmarks obtains 1%-to-3% improvement in dice coefficient compared to widely used activations for segmentation tasks. The superior performance and adaptability of ASAU highlight its potential for integration into a wide range of image classification and segmentation tasks.