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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 …
Deep learning in cancer diagnosis, prognosis and treatment selection
Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …
technique called artificial neural networks to extract patterns and make predictions from …
[HTML][HTML] Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …
grows, especially in high-stakes decision making areas such as medical image analysis …
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) …
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma
T Chanda, K Hauser, S Hobelsberger… - Nature …, 2024 - nature.com
Artificial intelligence (AI) systems have been shown to help dermatologists diagnose
melanoma more accurately, however they lack transparency, hindering user acceptance …
melanoma more accurately, however they lack transparency, hindering user acceptance …
[HTML][HTML] Explainable artificial intelligence in skin cancer recognition: A systematic review
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 …
[HTML][HTML] Pruning by explaining: A novel criterion for deep neural network pruning
The success of convolutional neural networks (CNNs) in various applications is
accompanied by a significant increase in computation and parameter storage costs. Recent …
accompanied by a significant increase in computation and parameter storage costs. Recent …
[HTML][HTML] Hierarchical graph representations in digital pathology
Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens
highly depend on the phenotype and topological distribution of constituting histological …
highly depend on the phenotype and topological distribution of constituting histological …
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 …
A generalized deep learning framework for whole-slide image segmentation and analysis
Histopathology tissue analysis is considered the gold standard in cancer diagnosis and
prognosis. Whole-slide imaging (WSI), ie, the scanning and digitization of entire histology …
prognosis. Whole-slide imaging (WSI), ie, the scanning and digitization of entire histology …