Toward explainable artificial intelligence for precision pathology
The rapid development of precision medicine in recent years has started to challenge
diagnostic pathology with respect to its ability to analyze histological images and …
diagnostic pathology with respect to its ability to analyze histological images and …
Explaining deep neural networks and beyond: A review of methods and applications
With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …
From attribution maps to human-understandable explanations through concept relevance propagation
The field of explainable artificial intelligence (XAI) aims to bring transparency to today's
powerful but opaque deep learning models. While local XAI methods explain individual …
powerful but opaque deep learning models. While local XAI methods explain individual …
Explainable AI methods-a brief overview
Abstract Explainable Artificial Intelligence (xAI) is an established field with a vibrant
community that has developed a variety of very successful approaches to explain and …
community that has developed a variety of very successful approaches to explain and …
Toward explainable artificial intelligence for regression models: A methodological perspective
In addition to the impressive predictive power of machine learning (ML) models, more
recently, explanation methods have emerged that enable an interpretation of complex …
recently, explanation methods have emerged that enable an interpretation of complex …
[HTML][HTML] Beyond explaining: Opportunities and challenges of XAI-based model improvement
Abstract Explainable Artificial Intelligence (XAI) is an emerging research field bringing
transparency to highly complex and opaque machine learning (ML) models. Despite the …
transparency to highly complex and opaque machine learning (ML) models. Despite the …
Making deep neural networks right for the right scientific reasons by interacting with their explanations
Deep neural networks have demonstrated excellent performances in many real-world
applications. Unfortunately, they may show Clever Hans-like behaviour (making use of …
applications. Unfortunately, they may show Clever Hans-like behaviour (making use of …
Towards best practice in explaining neural network decisions with LRP
Within the last decade, neural network based predictors have demonstrated impressive-and
at times superhuman-capabilities. This performance is often paid for with an intransparent …
at times superhuman-capabilities. This performance is often paid for with an intransparent …
Understanding the (extra-) ordinary: Validating deep model decisions with prototypical concept-based explanations
Ensuring both transparency and safety is critical when deploying Deep Neural Networks
(DNNs) in high-risk applications such as medicine. The field of explainable AI (XAI) has …
(DNNs) in high-risk applications such as medicine. The field of explainable AI (XAI) has …
Additive mil: Intrinsically interpretable multiple instance learning for pathology
Abstract Multiple Instance Learning (MIL) has been widely applied in pathology towards
solving critical problems such as automating cancer diagnosis and grading, predicting …
solving critical problems such as automating cancer diagnosis and grading, predicting …