Deep unsupervised approaches are gathering increased attention for applications such as
pathology detection and segmentation in medical images since they promise to alleviate the …

Abstract The Segment Anything Model (SAM) has demonstrated remarkable capabilities of
scaled-up segmentation models, enabling zero-shot generalization across a variety of …

Radiologists are tasked with interpreting a large number of images in a daily base, with the
responsibility of generating corresponding reports. This demanding workload elevates the …

The diagnosis is influenced by the classification of brain MRIs. Classifying and analyzing
structures within images can be significantly enhanced by employing the Swin Transformer …

Automated brain tumor segmentation based on deep learning (DL) has achieved promising
performance. However, it generally relies on annotated images for model training, which is …

Generating pseudo-healthy reconstructions of images is an effective way to detect
anomalies, as identifying the differences between the reconstruction and the original can …

The advanced deep learning-based Autoencoding techniques have enabled the
introduction of efficient Unsupervised Anomaly Detection (UAD) approaches. Several …

Background Current artificial intelligence studies for supporting CT screening tasks depend
on either supervised learning or detecting anomalies. However, the former involves a heavy …

Traumatic brain injury (TBI), a disruption of brain function caused by external forces to the
head, is a leading cause of death and disability for trauma patients in the world. In TBI …

The Segment Anything Model (SAM) has demonstrated remarkable capabilities of scaled-up
segmentation models, enabling zeroshot generalization across a variety of domains. By …