BMAI
バイオメディカルAI研究ユニット
バイオメディカルAI研究ユニット
Ja
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...
We developed an automated scheme for segmenting and calculating liver volume in hepatic CT by means of 3D fast-marching and level-set segmentation algorithms. Automatic liver segmentation on hepatic CT images is challenging because the liver often abuts other organs of similar density. Our purpose was to develop an automated liver segmentation scheme based on a 3D level-set algorithm for measur Read more...
Enhanced Digital Chest Radiography: Temporal Subtraction Combined with Virtual Dual-EnergyETechnology for Improved Conspicuity of Growing Cancers and Other Pathologic Changes
We developed a novel temporal-subtraction (TS) technique combined with virtual dual-energyEtechnology for improved conspicuity of growing cancers and other pathologic changes in digital chest radiography (CXR). Digi Read more...
– A CAD Utilizing 3D Massive-Training ANNs for Detection of Flat Lesions in CT Colonography in a Large Multicenter Clinical Trial
– Polyp Detection in CT Colonography: Performance of a CAD Scheme Incorporating 3D MTANNs on False-Negative Polyps in a Multicenter Clinical Trial
– Ensemble Training for a Mixture of Expert 3D MTANNs for Eliminating Multiple False-Positive Source Read more...
– Automated CT Liver Volumetry by Use of Three-Dimensional Fast-Marching and Level-Set Segmentation
– Reduction of Quantum Noise in Low-Dose Double-Contrast Radiographs of the Stomach
– Enhanced Digital Chest Radiography: Temporal Subtraction Combined with Virtual Dual-EnergyETechnology for Improved Conspicuity of Growing Cancers and Other Pathologic Changes
– Virtu Read more...
– A CAD Utilizing 3D Massive-Training ANNs for Detection of Flat Lesions in CT Colonography in a Large Multicenter Clinical Trial
– Polyp Detection in CT Colonography: Performance of a CAD Scheme Incorporating 3D MTANNs on False-Negative Polyps in a Multicenter Clinical Trial
– Ensemble Training for a Mixture of Expert 3D MTANNs for Eliminating Multiple False-Positive Source Read more...
– Automated CT Liver Volumetry by Use of Three-Dimensional Fast-Marching and Level-Set Segmentation
– Reduction of Quantum Noise in Low-Dose Double-Contrast Radiographs of the Stomach
– Enhanced Digital Chest Radiography: Temporal Subtraction Combined with Virtual Dual-EnergyETechnology for Improved Conspicuity of Growing Cancers and Other Pathologic Changes
– Virtu Read more...
– A CAD Utilizing 3D Massive-Training ANNs for Detection of Flat Lesions in CT Colonography in a Large Multicenter Clinical Trial
– Polyp Detection in CT Colonography: Performance of a CAD Scheme Incorporating 3D MTANNs on False-Negative Polyps in a Multicenter Clinical Trial
– Ensemble Training for a Mixture of Expert 3D MTANNs for Eliminating Multiple False-Positive Source Read more...