BACKGROUND: Given the recent increased emphasis on multimodal neural networks to solve complex modeling tasks, the problem of outcome prediction for a course of treatment can be framed as fundamentally multimodal in nature. A patient's response to tr...
BACKGROUND: Magnetic resonance imaging (MRI) is a highly sensitive modality for diagnosing breast cancer, providing an expanding range of clinical usages that are crucial for the care of women at elevated risk of breast cancer development. Segmentati...
BACKGROUND: Sparse-view CT shortens scan time and reduces radiation dose but results in severe streak artifacts due to insufficient sampling data. Deep learning methods can now suppress these artifacts and improve image quality in sparse-view CT reco...
BACKGROUND: Adaptive radiotherapy (ART) can compensate for the dosimetric impact of anatomic change during radiotherapy of head-neck cancer (HNC) patients. However, implementing ART universally poses challenges in clinical workflow and resource alloc...
BACKGROUND: Proton pencil beam scanning (PBS) treatment planning for head and neck (H&N) cancers is a time-consuming and experience-demanding task where a large number of potentially conflicting planning objectives are involved. Deep reinforcement le...
BACKGROUND: Breast cancer screening via mammography plays a crucial role in early detection, significantly impacting women's health outcomes worldwide. However, the manual analysis of mammographic images is time-consuming and requires specialized exp...
BACKGROUND: Computed tomography angiography (CTA) is used to screen for coronary artery calcification. As the coronary artery has complicated structure and tiny lumen, manual screening is a time-consuming task. Recently, many deep learning methods ha...
BACKGROUND: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major ...
BACKGROUND: Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in suc...