A key challenge in cancer immunotherapy biomarker research is quantification of pattern changes in microscopic whole slide images of tumour biopsies. Drug development requires a …
Endoscopic video frames tend to be corrupted by various artefacts impairing their visibility. Automated detection of these artefacts will foster advances in computer-assisted …
Objective: Our aim is to decompose a conventional X-ray image into several independent, non-overlapping depth layers, such that the sum resembles the original input.Material and …
Fusion of preoperative data with intraoperative X-ray images has proven the potential to reduce radiation exposure and contrast agent, especially for complex endovascular aortic …
Artificial intelligence (AI) and machine learning (ML) have influenced medicine in myriad ways, and medical imaging is at the forefront of technological transformation. Recent …
One of the major challenges currently facing researchers in applying deep learning (DL) models to medical image analysis is the limited amount of annotated data. Collecting such …
Clinical procedures that make use of fluoroscopy may expose patients as well as the clinical staff (throughout their career) to non-negligible doses of radiation. The potential …
Background: MR imaging is a mainstay of MS disease activity as-sessment. However, lesion detection is time-consuming and subject to relevant inter-observer variability. We have …
Importance: Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective: Assess the performance of …