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.

本研究提出了一种新的激活函数，称为自适应平滑激活单元（ASAU），专为优化梯度传播而设计，从而提高了卷积神经网络在医学图像分析中的效能。我们将这个新的激活函数应用于医学图像分析中的两个重要和常用的任务：CT 和 MRI 中的自动疾病诊断和器官分割。在 RadImageNet 腹部 / 盆腔（CT 和 MRI）数据集和 2017 年 LiTS（肝肿瘤分割基准）上进行了严格评估，结果显示我们整合了 ASAU 的框架不仅在腹部 CT 和 MRI 的分类准确性（疾病检测）上取得了较大的改善（4.80％），而且与广泛使用的 “健康肝组织” 分割的激活方法相比，dice 系数也有 1％-3％的提升。这些改进为开发诊断工具，特别针对复杂且具有挑战性的病理提供了新的基准。ASAU 的优越性能和适应性凸显了它在各种图像分类和分割任务中的潜力。