  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 convolutional 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. Our rigorous evaluation on the RadImageNet
abdominal/pelvis (CT and MRI) dataset and Liver Tumor Segmentation Benchmark
(LiTS) 2017 demonstrates that our ASAU-integrated frameworks not only achieve a
substantial (4.80\%) improvement over ReLU in classification accuracy (disease
detection) on abdominal CT and MRI but also achieves 1\%-3\% improvement in
dice coefficient compared to widely used activations for `healthy liver tissue'
segmentation. These improvements offer new baselines for developing a
diagnostic tool, particularly for complex, challenging pathologies. The
superior performance and adaptability of ASAU highlight its potential for
integration into a wide range of image classification and segmentation tasks.
