Special Issue "Interpretable Deep Learning in Electronics, Computer Science and Medical Imaging"
Deadline for manuscript submissions: closed (31 August 2020).
Interests: artificial intelligence; deep learning; classification; rule extraction; big data analytics; interpretability of deep neural network
Special Issues and Collections in MDPI journals
Artificial intelligence (AI), particularly, deep learning (DL), which involves automated feature extraction using deep neural networks (DNNs), has been used increasingly by electronics engineer, computer scientist, and physicians. AI can analyze computer vision and medical images at a level not possible by a single person. However, the resulting parameters are difficult to interpret. This so-called “black box” problem causes opaqueness in DL.
The aim of the Special Issue is to help realize interpretable DL in electronics, computer science, and medical imaging. To achieve this aim, we should attempt to bring about a paradigm shift in electronics, computer science, and medical imaging in which diagnostic accuracy is surpassed to achieve explainability. DL in medical imaging has still considerable limitations. To interpret and apply DL to medical imaging tasks effectively, sufficient expertise in computer science is required. We should interpret elements of decision-making behind classification decisions. While DL algorithms can markedly enhance the quantitative performance, such as accuracy, interpretability is a vital component.
Moreover, establishing accountability is one of the most important issues in medical imaging to explain the classification results clearly. Although more interpretable algorithms seem likely to be more readily accepted by electronics, computers science, and medical professionals, it remains necessary to determine whether this could increase effectiveness in electronics, computer science, and medical imaging. For the acceptance of AI by electronics engineers, computer scientists, and physicians, not only quantitative, but also qualitative algorithmic performance should be improved.
Topics of interest of this Special Issue include, but are not limited to:
- Interpretable DL in electronics
- Interpretable DL in computer science
- Interpretable DL in medical imaging
- Non-black-box machine learning
- Interpretable large decision trees and random forests
- Interpretable machine learning
- Converting deep neural network to decision trees
- Interpretable decision trees
Prof. Dr. Yoichi Hayashi
Manuscript Submission Information
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