@inproceedings{fa44dce0bd684b9f9a5b785a634a824c,
title = "SIDU: Similarity Difference And Uniqueness Method for Explainable AI",
abstract = "A new brand of technical artificial intelligence ( Explainable AI ) research has focused on trying to open up the {\textquoteright}black box{\textquoteright} and provide some explainability. This paper presents a novel visual explanation method for deep learning networks in the form of a saliency map that can effectively localize entire object regions. In contrast to the current state-of-the art methods, the proposed method shows quite promising visual explanations that can gain greater trust of human expert. Both quantitative and qualitative evaluations are carried out on both general and clinical data sets to confirm the effectiveness of the proposed method.",
keywords = "Explainable AI, Deep Learning, Interpretability, Explainability",
author = "Muddamsetty, {Satya Mahesh} and Jahromi, {Mohammad Naser Sabet} and Moeslund, {Thomas B.}",
year = "2020",
doi = "10.1109/ICIP40778.2020.9190952",
language = "English",
isbn = "978-1-7281-6394-9",
series = "IEEE International Conference on Image Processing (ICIP)",
booktitle = "2020 IEEE International Conference on Image Processing (ICIP)",
publisher = "IEEE",
address = "United States",
note = "2020 IEEE International Conference on Image Processing (ICIP) ; Conference date: 25-10-2020 Through 28-10-2020",
}