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In this study, we propose a new activation function, called Adaptive SmoothActivation Unit (ASAU), tailored for optimized gradient propagation, therebyenhancing the proficiency of convolutional networks in medical image analysis.We apply this new activation function to two important and commonly usedgeneral tasks in medical image analysis: automatic disease diagnosis and organsegmentation in CT and MRI. Our rigorous evaluation on the RadImageNetabdominal/pelvis (CT and MRI) dataset and Liver Tumor Segmentation Benchmark(LiTS) 2017 demonstrates that our ASAU-integrated frameworks not only achieve asubstantial (4.80\%) improvement over ReLU in classification accuracy (diseasedetection) on abdominal CT and MRI but also achieves 1\%-3\% improvement indice coefficient compared to widely used activations for `healthy liver tissue'segmentation. These improvements offer new baselines for developing adiagnostic tool, particularly for complex, challenging pathologies. Thesuperior performance and adaptability of ASAU highlight its potential forintegration into a wide range of image classification and segmentation tasks.

Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans