Medical image synthesis represents a critical area of research in clinical decision-making,
aiming to overcome the challenges associated with acquiring multiple image modalities for …

Deep learning (DL) in orthopaedics has gained significant attention in recent years.
Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks …

In recent years, considerable work has been conducted on the development of synthetic
medical images, but there are no satisfactory methods for evaluating their medical suitability …

Previous studies on automating building design with deep learning primarily focused on
planning room layouts, limiting the design of architectural elements such as doors and …

We investigate the role of the loss function in cycle consistency generative adversarial
networks (CycleGANs). Namely, the sliced Wasserstein distance is proposed for this type of …

Any machine learning (ML) model is highly dependent on the data it uses for learning, and
this is even more important in the case of deep learning models. The problem is a data …

Generative Adversarial Networks are a class of artificial intelligence algorithms that consist
of a generator and a discriminator trained simultaneously through adversarial training. GANs …

Lung cancer represents a significant global health challenge, transcending demographic
boundaries of age, gender, and ethnicity. Timely detection stands as a pivotal factor for …

Human-in-the-Loop Machine Learning (HITL-ML) is a set of techniques that attempt to
actively introduce experts into the learning loop of machine learning (ML) models to improve …

Medical image generation has recently garnered significant interest among researchers.
However, the primary generative models, such as Generative Adversarial Networks (GANs) …