The purpose of this project is to develop computer-aided diagnosis/detection (CAD) for a wide variety of radiologic images and disease types. This project uses existing NIH CT scan images.? ? We published the first (to our knowledge) subcutaneous melanoma CAD system. We are now developing a CAD system to detect spinal metastases on CT. In preliminary evaluation, this CAD system is effective at detecting large lytic spinal metastases. With further development, this system may prevent false negative diagnoses of lytic spinal metastases.
| Summers, Ronald M (2018) Deep Learning Lends a Hand to Pediatric Radiology. Radiology 287:323-325 |
| Burns, Joseph E; Yao, Jianhua; Summers, Ronald M (2017) Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images. Radiology 284:788-797 |
| Burns, Joseph E; Yao, Jianhua; Muñoz, Hector et al. (2016) Automated Detection, Localization, and Classification of Traumatic Vertebral Body Fractures in the Thoracic and Lumbar Spine at CT. Radiology 278:64-73 |
| O'Connor, Stacy D; Yao, Jianhua; Summers, Ronald M (2007) Lytic metastases in thoracolumbar spine: computer-aided detection at CT--preliminary study. Radiology 242:811-6 |
| Solomon, Jeffrey; Mavinkurve, Sara; Cox, Derrick et al. (2004) Computer-assisted detection of subcutaneous melanomas: feasibility assessment. Acad Radiol 11:678-85 |
| Summers, Ronald M (2003) Road maps for advancement of radiologic computer-aided detection in the 21st century. Radiology 229:11-3 |
