[HTML][HTML] Transparency of deep neural networks for medical image analysis: A review of interpretability methods
Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …
diagnosis and treatment decisions. Deep neural networks have shown the same or better …
[HTML][HTML] Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review
Abstract Transparency in Machine Learning (ML), often also referred to as interpretability or
explainability, attempts to reveal the working mechanisms of complex models. From a …
explainability, attempts to reveal the working mechanisms of complex models. From a …
Interpretable image classification with differentiable prototypes assignment
D Rymarczyk, Ł Struski, M Górszczak… - … on Computer Vision, 2022 - Springer
Existing prototypical-based models address the black-box nature of deep learning.
However, they are sub-optimal as they often assume separate prototypes for each class …
However, they are sub-optimal as they often assume separate prototypes for each class …
[HTML][HTML] Learning disentangled representations in the imaging domain
Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …
general representations even in the absence of, or with limited, supervision. A good general …
Protopshare: Prototypical parts sharing for similarity discovery in interpretable image classification
In this work, we introduce an extension to ProtoPNet called ProtoPShare which shares
prototypical parts between classes. To obtain prototype sharing we prune prototypical parts …
prototypical parts between classes. To obtain prototype sharing we prune prototypical parts …
[HTML][HTML] A survey on the interpretability of deep learning in medical diagnosis
Q Teng, Z Liu, Y Song, K Han, Y Lu - Multimedia Systems, 2022 - Springer
Deep learning has demonstrated remarkable performance in the medical domain, with
accuracy that rivals or even exceeds that of human experts. However, it has a significant …
accuracy that rivals or even exceeds that of human experts. However, it has a significant …
FUTURE-AI: guiding principles and consensus recommendations for trustworthy artificial intelligence in medical imaging
The recent advancements in artificial intelligence (AI) combined with the extensive amount
of data generated by today's clinical systems, has led to the development of imaging AI …
of data generated by today's clinical systems, has led to the development of imaging AI …
Explainable artificial intelligence and cardiac imaging: toward more interpretable models
Artificial intelligence applications have shown success in different medical and health care
domains, and cardiac imaging is no exception. However, some machine learning models …
domains, and cardiac imaging is no exception. However, some machine learning models …
Echocardiography segmentation with enforced temporal consistency
Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac
ultrasound images. However, despite recent successes according to which the intra …
ultrasound images. However, despite recent successes according to which the intra …
Interpretability benchmark for evaluating spatial misalignment of prototypical parts explanations
Prototypical parts-based networks are becoming increasingly popular due to their faithful
self-explanations. However, their similarity maps are calculated in the penultimate network …
self-explanations. However, their similarity maps are calculated in the penultimate network …