Latest AI and machine learning research in breast cancer for healthcare professionals.
Accurate and precise positioning of the acetabular cup remains a prevalent challenge in total hip ar...
BACKGROUND: Reducing the radiation dose from computed tomography (CT) can significantly reduce the r...
An otherwise well 28-month-old girl presented with fever/left thigh pain. Computed tomography identi...
Cannulated screw fixation is the main therapy for femoral neck fractures, especially in young patien...
In 1971, the first computed tomography (CT) scan was performed on a patient's brain. Clinical CT sys...
Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways v...
PURPOSE: To develop a deep learning model that combines CT and radiation dose (RD) images to predict...
Tumor segmentation is a fundamental step for radiotherapy treatment planning. To define an accurate...
. The purpose of this study was to evaluate the accuracy of brachytherapy (BT) planning structures d...
Deep learning (DL) is one of the most powerful data-driven machine-learning techniques in artificial...
Deep learning-based in silico alternatives have been demonstrated to be of significant importance in...
BACKGROUND: Cone beam computed tomography (CBCT) plays an increasingly important role in image-guide...
From the widespread use of smartphones and tablets to the multitude of applications available, older...
This paper introduces the adaptive fuzzy control scheme as a promising control technique for cancer ...
PURPOSE: To investigate the use of an 80-kVp tube voltage combined with a deep learning image recons...
BACKGROUND: Optical scanning technologies are increasingly being utilised to supplement treatment wo...
The era of high-throughput techniques created big data in the medical field and research disciplines...
OBJECTIVE: Molecular subtyping is an important procedure for prognosis and targeted therapy of breas...
The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learni...
Self-attention mechanism-based algorithms are attractive in digital pathology due to their interpret...
Particle size, shape and morphology can be considered as the most significant functional parameters,...