Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …

Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …

Anomaly detection has become an indispensable tool for modern society, applied in a wide
range of applications, from detecting fraudulent transactions to malignant brain tumors. Over …

Adversarial attacks are considered a potentially serious security threat for machine learning
systems. Medical image analysis (MedIA) systems have recently been argued to be …

Deep learning techniques have promoted the rise of artificial intelligence (AI) and performed
well in computer vision. Medical image analysis is an important application of deep learning …

Chest computed tomography (CT) image data is necessary for early diagnosis, treatment,
and prognosis of Coronavirus Disease 2019 (COVID-19). Artificial intelligence has been …

The performance of current machine learning methods to detect heterogeneous pathology is
limited by the quantity and quality of pathology in medical images. A possible solution is …

Artificial intelligence techniques involving the use of artificial neural networks—that is, deep
learning techniques—are expected to have a major effect on radiology. Some of the most …

Oral squamous cell carcinoma (OSCC) has the characteristics of early regional lymph node
metastasis. OSCC patients often have poor prognoses and low survival rates due to cervical …