Search Results for author:
Found 9 papers, 5 papers with code
Vision Foundation Models for Computed Tomography
Foundation models (FMs) have shown transformative potential in radiology by performing diverse, complex tasks across imaging modalities.
Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection
For the second cohort, follow-up data of 311 patients was used to examine progression prediction capabilities.
Magnetic resonance delta radiomics to track radiation response in lung tumors receiving stereotactic MRI-guided radiotherapy
Thus, we explore the potential of delta radiomics from on-treatment magnetic resonance (MR) imaging to track radiation dose response, inform personalized radiotherapy dosing, and predict outcomes.
The impact of responding to patient messages with large language model assistance
Results show promise for AI to improve clinician efficiency and patient care through assisting documentation, if used judiciously.
mage based prognosis in head and neck cancer using convolutional neural networks: a case study in reproducibility and optimization
Although there have been improvements in the reproducibility of deep learning models, our work suggests that wider implementation of reporting standards is required to avoid a reproducibility crisis.
Deep Learning-based Assessment of Hepatic Steatosis on chest CT
A dataset of 451 CT scans with volumetric liver segmentations of expert readers was used for training a deep learning model.
Deep learning-based detection of intravenous contrast in computed tomography scans
The fine-tuned model on chest CTs yielded an AUC: 1. 0 for the internal validation set (n = 53), and AUC: 0. 980 for the external validation set (n = 402).
The importance of transparency and reproducibility in artificial intelligence research
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening.
Applications
Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer
In this work, 1615 radiomic features (quantifying tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET and CT images of 300 patients from four different cohorts were analyzed for the risk assessment of locoregional recurrences (LR) and distant metastases (DM) in head-and-neck cancer.
