5 papers
Low performing pixel correction in computed tomography with unrolled network and synthetic data training
Hongxu Yang, Levente Lippenszky, Edina Timko +2
Low performance pixels (LPP) in Computed Tomography (CT) detectors would lead to ring and streak artifacts in the reconstructed images, making them clinically unusable. In recent y…
SynBT: High-quality Tumor Synthesis for Breast Tumor Segmentation by 3D Diffusion Model
Hongxu Yang, Edina Timko, Levente Lippenszky +2
Synthetic tumors in medical images offer controllable characteristics that facilitate the training of machine learning models, leading to an improved segmentation performance. Howe…
Quality Enhancement of Radiographic X-ray Images by Interpretable Mapping
Hongxu Yang, Najib Akram Aboobacker, Xiaomeng Dong +3
X-ray imaging is the most widely used medical imaging modality. However, in the common practice, inconsistency in the initial presentation of X-ray images is a common complaint by…
Automated Identification of Failure Cases in Organ at Risk Segmentation Using Distance Metrics: A Study on CT Data
Amin Honarmandi Shandiz, Attila Rádics, Rajesh Tamada +5
Automated organ at risk (OAR) segmentation is crucial for radiation therapy planning in CT scans, but the generated contours by automated models can be inaccurate, potentially lead…
Pristine annotations-based multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19
Tao Tan, Bipul Das, Ravi Soni +13
The COVID-19 pandemic continues to spread and impact the well-being of the global population. The front-line modalities including computed tomography (CT) and X-ray play an importa…