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BMAI

Biomedical Artificial
Intelligence Research Unit

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Computerized Detection of Lung Nodules in Low-Dose CT, Part II: Usefulness of Multiple Massive-Training Artificial Neural Networks (Multi-MTANNs)

Computerized Detection of Lung Nodules in Low-Dose CT, Part II: Usefulness of Multiple Massive-Training Artificial Neural Networks (Multi-MTANNs)

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...

Research Area :
DML
Clinical Area :
CR
Anatomy :
Chest
Massive-Training Artificial Neural Network (MTANN) Trained with a Small Number of Cases for Enhancement of Nodules and Suppression of Vessels in Thoracic CT: Phantom Experiments

Massive-Training Artificial Neural Network (MTANN) Trained with a Small Number of Cases for Enhancement of Nodules and Suppression of Vessels in Thoracic CT: Phantom Experiments

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...

Research Area :
DML
Clinical Area :
CR
Anatomy :
Chest