The volume of medical images stored in hospitals is rapidly increasing; however, the
utilization of these accumulated medical images remains limited. Existing content-based …

The synthesis of pseudo-healthy images, involving the generation of healthy counterparts for
pathological images, is crucial for data augmentation, clinical disease diagnosis, and …

Large volume of labeled data is a cornerstone for deep learning (DL) based segmentation
methods. Medical images require domain experts to annotate, and full segmentation …

Objective. Accurate left atrial segmentation is the basis of the recognition and clinical
analysis of atrial fibrillation. Supervised learning has achieved some competitive …

The artifacts in histology images may encumber the accurate interpretation of medical
information and cause misdiagnosis. Accordingly, prepending manual quality control of …

Abstract Quantitative T2‐weighted MRI (T2W) interpretation is impeded by the variability of
acquisition‐related features, such as field strength, coil type, signal amplification, and pulse …

Abstract (EN) State-of-the-art machine learning methods such as convolutional neural
networks (CNNs) are frequently employed in computer vision. Despite their high …

Positron emission tomography (PET) imaging is an invaluable tool in clinical settings as it
captures the functional activity of both healthy anatomy and cancerous lesions. Developing …

Purpose The aim of this study is to develop a framework for quantitative analysis of
longitudinal T2-weighted MRIs (T2w) following radiotherapy (RT) for prostate cancer …