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Found 7 papers, 0 papers with code
Performance Deterioration of Deep Learning Models after Clinical Deployment: A Case Study with Auto-segmentation for Definitive Prostate Cancer Radiotherapy
The bladder contour quality was primarily affected by using IV contrast.
Uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective
Especially for smaller, single institutional datasets, it may be important to evaluate multiple estimations techniques before incorporating a model into clinical practice.
Region Specific Optimization (RSO)-based Deep Interactive Registration
A test time optimization (TTO) technique was proposed to further improve the DL models' performance.
Deep Dose Plugin Towards Real-time Monte Carlo Dose Calculation Through a Deep Learning based Denoising Algorithm
As a result, the whole MC dose calculation pipeline can be finished within 0. 15 seconds, including both GPU MC dose calculation and deep learning based denoising, achieving the real time efficiency needed for some radiotherapy applications, such as online adaptive radiotherapy.
Deep Interactive Denoiser (DID) for X-Ray Computed Tomography
However, there exists two challenges regarding the DL-based denoisers: 1) a trained model typically does not generate different image candidates with different noise-resolution tradeoffs which sometimes are needed for different clinical tasks; 2) the model generalizability might be an issue when the noise level in the testing images is different from that in the training dataset.
Probabilistic self-learning framework for Low-dose CT Denoising
Despite the indispensable role of X-ray computed tomography (CT) in diagnostic medicine field, the associated ionizing radiation is still a major concern considering that it may cause genetic and cancerous diseases.
