Large enough to train a deep neural network, the DeepLesion database could enable the scientific community to create a large-scale universal lesion detector with one unified framework, according to an NIH statement. The proposed lesion detector achieves a sensitivity of 81.1% with five false positives per image, the researchers wrote in their study, published in the Journal of Medical Imaging.
DeepLesion contains 32,735 lesions in 32,120 CT slices from 10,594 studies of 4,427 unique, anonymized patients. It contains a variety of lesion types, including lung nodules, liver tumors and enlarged lymph nodes. Most publicly available medical image datasets have fewer than 1,000 images, according to NIH’s Clinical Center, where the research took place.