Microvascular invasion (MVI) has been clinically recognized as a prognostic factor for
hepatocellular carcinoma (HCC) after surgical treatment. Detection of MVI before surgical …

It is a mystery which input features contribute to a neural network's output. Various
explanations methods are proposed in the literature to shed light on the problem. One …

Explainability is a key requirement for computer-aided diagnosis systems in clinical decision-
making. Multiple instance learning with attention pooling provides instance-level …

Deep learning models used in medical image analysis are prone to raising reliability
concerns due to their black-box nature. To shed light on these black-box models, previous …

Deep learning-based approaches for content-based image retrieval (CBIR) of computed
tomography (CT) liver images is an active field of research, but suffer from some critical …

Computerized tomography (CT) is a clinically primary technique to differentiate benign-
malignant pulmonary nodules for lung cancer diagnosis. Early classification of pulmonary …

Interpretability is essential for machine learning algorithms in high-stakes application fields
such as medical image analysis. However, high-performing black-box neural networks do …

Chest computed tomography (CT) has played an essential diagnostic role in assessing
patients with COVID-19 by showing disease-specific image features such as ground-glass …

Do black-box neural network models learn clinically relevant features for fracture diagnosis?
The answer not only establishes reliability, quenches scientific curiosity, but also leads to …

Data augmentation strategies are actively used when training deep neural networks (DNNs).
Recent studies suggest that they are effective at various tasks. However, the effect of data …