bibtext: @InProceedings{ Bis_Adaptive_MICCAI2024,
                   author = { Biswas, Koushik and Jha, Debesh and Tomar, Nikhil Kumar and Karri, Meghana and Reza, Amit and Durak, Gorkem and Medetalibeyoglu, Alpay and Antalek, Matthew and Velichko, Yury and Ladner, Daniela and Borhani, Amir and Bagci, Ulas },
                   title = { { Adaptive Smooth Activation Function for Improved Organ Segmentation and Disease Diagnosis } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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 \textit{(\textbf{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 \textit{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. The code is available at \href{https://github.com/koushik313/ASAU}{https://github.com/koushik313/ASAU}.
title: Adaptive Smooth Activation Function for Improved Organ Segmentation and Disease Diagnosis
authors: Biswas, Koushik Jha, Debesh Tomar, Nikhil Kumar Karri, Meghana Reza, Amit Durak, Gorkem Medetalibeyoglu, Alpay Antalek, Matthew Velichko, Yury Ladner, Daniela Borhani, Amir Bagci, Ulas