BMAI
Biomedical Artificial
Intelligence Research Unit
Biomedical Artificial
Intelligence Research Unit
En
AI-aided Diagnosis
A major challenge in computer-aided diagnostic (CAD) schemes for lung nodule detection in multi-detector-row CT (MDCT) is to reduce false positives (FPs) while maintaining a high sensitivity level. Our purpose in this study was to develop a three-dimensional (3D) massive-training artificial neural network (MTANN) for reduction of FPs. To process quasi-isotropic voxels in the 3D MDCT volume, we Read more...
We developed computer-aided diagnostic (CAD) scheme for detection of polyps in CT colonography (CTC) and evaluating our CAD scheme with false-negative polyps in a large multicenter clinical trial in collaboration with Don C. Rockey, M.D., the Southwest Medical Center at the University of Texas. A major challenge in CAD schemes for detection of polyps in CTC is the detection of difficultEpolyps Read more...
A CAD Utilizing 3D Massive-Training ANNs for Detection of Flat Lesions in CT Colonography in a Large Multicenter Clinical Trial
We developed a computer-aided diagnostic (CAD) scheme for detection of flat lesions (also called flat polyps or depressed polyps) in CT colonography (CTC) in a large multicenter clinical trial in collaboration with Don C. Rockey, M.D., at the Southwest Medical C 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...
– 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...