Biomedical Artificial
Intelligence Research Unit
En
Deep / Machine Learning
We extended the capability of a single massive-training artificial neural network (MTANN) and developed a multiple MTANN scheme (multi-MTANN) for further removal of false positives (FPs) in computerized detection of lung nodules in low-dose CT. The multi-MTANN consists of several MTANNs arranged in parallel. Each MTANN is trained by use of the same nodules, but with a different type of non-nodu Read more...
A massive-training artificial neural network (MTANN) is a trainable, highly nonlinear filter consisting of a linear-output multilayer artificial neural network model. For enhancement of nodules and suppression of vessels, we used 10 nodules and 10 non-nodule images as training cases for MTANNs. The MTANN is trained with a large number of input subregions selected from the training cases and the Read more...
We investigated a novel pattern-recognition technique based on an artificial neural network (ANN), called a massive-training artificial neural network (MTANN), for reduction of false positives (FPs) in computerized detection of lung nodules in low-dose CT. The MTANN consists of a linear-output multilayer ANN model, which is capable of operating on image data directly. The MTANN is trained by us Read more...