Ensuring confidence in neural network decisions in medical diagnostics based on visual data
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Дата
2024
Назва журналу
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Назва тому
Видавець
Анотація
The paper presents a novel method for medical image analysis that combines the high
accuracy of deep learning models and the interpretability of logical models. The proposed approach
involves training a convolutional neural network (CNN) for accurate image classification, applying a
spatial attention mechanism to localize important features, and constructing an interpretable Decision
Rule Network (DRN) based on these features. The DRN is a set of logical rules linking feature values
to diagnoses, allowing for transparent decision-making.
The method was evaluated on brain MRI scans, achieving high accuracy with the CNN (>95%) and
interpretability with the DRN. The authors emphasize the importance of achieving consistency between
the CNN and DRN decisions for specific clinical cases, ensuring trust and compliance with ethical and
regulatory requirements in medical AI applications.
Опис
Ключові слова
medical image analysis, convolutional neural networks, explainable AI, Decision Rule Network, interpretability
Бібліографічний опис
Manziuk E., Skrypnyk T., Lukmanov T.,Kyrychenko O. Ensuring confidence in neural network decisions in medical diagnostics based on visual data // X International Conference “Ukrainian-Polish Scientific Dialogues. Actual problems of modern science 2024” Bydgoszcz – Khmelnytskyi, 2024. p. 521-525