With the phantom baseline and shrapnel-containing images acquired, we evaluated if the phantom can reduce the number of animal or human images required to properly train a deep learning image classification algorithm.
Oh et al. 6 developed and validated an interpretable deep learning model using a large and diverse dataset to objectively evaluate the quality of standard phantom image. The objective of this study was to assist radiologists in mammography phantom image evaluation by developing and validating an interpretable deep learning model capable of objectively evaluating the quality of standard phantom images for mammography. Overall, the tissue phantom demonstrated high performance for developing deep learning models for ultrasound image classification. Ultrasound view of the tissue phantom compared to swine tissue. The digital phantom voxel size was set to 1 mm in all directions. Additionally, 80 anthropomorphic digital breast phantoms were obtained from the work of Garca et al. 20 to be used as an extra dataset of digital phantom cases with realistic internal glandular and adipose texture to further fine-tune our model to be applied to a clinical case. Procedure for markerless tumour tracking by patient-specific deep learning in treatment workflow. We validated the feasibility of our strategy by evaluating tracking accuracy in both a digital phantom simulation study and an epoxy phantom study. Methods and materials Deep learning for markerless tracking We designed a neural network model (Figure 2) for two-class semantic segmentation based on ...
Such details provide a deeper understanding and appreciation for Phantom Modeling With Deep Learning.
In deep learning, a model learns to perform classification or regression tasks directly from data such as images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, often exceeding human-level performance.
As we can see from the illustration, Phantom Modeling With Deep Learning has many fascinating aspects to explore.
