[HTML][HTML] Explainable artificial intelligence in skin cancer recognition: A systematic review
K Hauser, A Kurz, S Haggenmüller, RC Maron… - European Journal of …, 2022 - Elsevier
Background Due to their ability to solve complex problems, deep neural networks (DNNs)
are becoming increasingly popular in medical applications. However, decision-making by …
are becoming increasingly popular in medical applications. However, decision-making by …
Explainable AI in medical imaging: An overview for clinical practitioners–Beyond saliency-based XAI approaches
Driven by recent advances in Artificial Intelligence (AI) and Computer Vision (CV), the
implementation of AI systems in the medical domain increased correspondingly. This is …
implementation of AI systems in the medical domain increased correspondingly. This is …
Learning concise and descriptive attributes for visual recognition
Recent advances in foundation models present new opportunities for interpretable visual
recognition--one can first query Large Language Models (LLMs) to obtain a set of attributes …
recognition--one can first query Large Language Models (LLMs) to obtain a set of attributes …
Post-hoc concept bottleneck models
Concept Bottleneck Models (CBMs) map the inputs onto a set of interpretable concepts
(``the bottleneck'') and use the concepts to make predictions. A concept bottleneck enhances …
(``the bottleneck'') and use the concepts to make predictions. A concept bottleneck enhances …
Toward transparent ai: A survey on interpreting the inner structures of deep neural networks
The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …
Using sequences of life-events to predict human lives
Here we represent human lives in a way that shares structural similarity to language, and we
exploit this similarity to adapt natural language processing techniques to examine the …
exploit this similarity to adapt natural language processing techniques to examine the …
Survey on explainable AI: From approaches, limitations and applications aspects
In recent years, artificial intelligence (AI) technology has been used in most if not all domains
and has greatly benefited our lives. While AI can accurately extract critical features and …
and has greatly benefited our lives. While AI can accurately extract critical features and …
Red teaming deep neural networks with feature synthesis tools
Interpretable AI tools are often motivated by the goal of understanding model behavior in out-
of-distribution (OOD) contexts. Despite the attention this area of study receives, there are …
of-distribution (OOD) contexts. Despite the attention this area of study receives, there are …
ExAID: A multimodal explanation framework for computer-aided diagnosis of skin lesions
Background and objectives: One principal impediment in the successful deployment of
Artificial Intelligence (AI) based Computer-Aided Diagnosis (CAD) systems in everyday …
Artificial Intelligence (AI) based Computer-Aided Diagnosis (CAD) systems in everyday …
Skincon: A skin disease dataset densely annotated by domain experts for fine-grained debugging and analysis
R Daneshjou, M Yuksekgonul, ZR Cai… - Advances in …, 2022 - proceedings.neurips.cc
For the deployment of artificial intelligence (AI) in high risk settings, such as healthcare,
methods that provide interpretability/explainability or allow fine-grained error analysis are …
methods that provide interpretability/explainability or allow fine-grained error analysis are …