Going beyond xai: A systematic survey for explanation-guided learning

Y Gao, S Gu, J Jiang, SR Hong, D Yu, L Zhao - ACM Computing Surveys, 2024 - dl.acm.org
As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing
DNNs become more complex and diverse, ranging from improving a conventional model …

[HTML][HTML] Explainable Artificial Intelligence (XAI) for Oncological Ultrasound Image Analysis: A Systematic Review

LS Wyatt, LM van Karnenbeek, M Wijkhuizen… - Applied Sciences, 2024 - mdpi.com
This review provides an overview of explainable AI (XAI) methods for oncological ultrasound
image analysis and compares their performance evaluations. A systematic search of …

The FIX Benchmark: Extracting Features Interpretable to eXperts

H **, S Havaldar, C Kim, A Xue, W You, H Qu… - arxiv preprint arxiv …, 2024 - arxiv.org
Feature-based methods are commonly used to explain model predictions, but these
methods often implicitly assume that interpretable features are readily available. However …

An Exploration on Explainable AI with Background and Motivation for XAI

BP Sheela, H Girisha - … Conference on Innovations in Cybersecurity and …, 2024 - Springer
This article deals with the exploration of XAI in the field of Image processing and Machine
Learning. Machine learning models are rapidly used to make important decisions in …

Explainability and Understandability of Artificial Neural Networks

J Stodt - 2024 - pearl.plymouth.ac.uk
Abstract Artificial Neural Networks (ANNs) have achieved significant success in fields
likehealthcare, but their" black box" nature challenges transparency and user trust. Existing …

An Exploration on Explainable AI with Background and Motivation for XAΙ

BP Sheela, H Girisha - … and Data Science: Proceedings of ICICDS …, 2024 - books.google.com
Explainable AI is an emerging field in artificial intelligence and machine learning that aims to
address how the decision-making process can be made transparent in ML model and quick …