  The success of deep learning relies heavily on large labeled datasets, but we
often only have access to several small datasets associated with partial
labels. To address this problem, we propose a new initiative, "Label-Assemble",
that aims to unleash the full potential of partial labels from an assembly of
public datasets. We discovered that learning from negative examples facilitates
both computer-aided disease diagnosis and detection. This discovery will be
particularly crucial in novel disease diagnosis, where positive examples are
hard to collect, yet negative examples are relatively easier to assemble. For
example, assembling existing labels from NIH ChestX-ray14 (available since
2017) significantly improves the accuracy of COVID-19 diagnosis from 96.3% to
99.3%. In addition to diagnosis, assembling labels can also improve disease
detection, e.g., the detection of pancreatic ductal adenocarcinoma (PDAC) can
greatly benefit from leveraging the labels of Cysts and PanNets (two other
types of pancreatic abnormalities), increasing sensitivity from 52.1% to 84.0%
while maintaining a high specificity of 98.0%.
