Early Detection and Localization of Pancreatic Cancer by Label-Free
  Tumor Synthesis
Bowen Li Yu-Cheng Chou Shuwen Sun Hualin Qiao Alan Yuille Zongwei Zhou
Early detection and localization of pancreatic cancer can increase the 5-year
survival rate for patients from 8.5% to 20%. Artificial intelligence (AI) can
potentially assist radiologists in detecting pancreatic tumors at an early
stage. Training AI models require a vast number of annotated examples, but the
availability of CT scans obtaining early-stage tumors is constrained. This is
because early-stage tumors may not cause any symptoms, which can delay
detection, and the tumors are relatively small and may be almost invisible to
human eyes on CT scans. To address this issue, we develop a tumor synthesis
method that can synthesize enormous examples of small pancreatic tumors in the
healthy pancreas without the need for manual annotation. Our experiments
demonstrate that the overall detection rate of pancreatic tumors, measured by
Sensitivity and Specificity, achieved by AI trained on synthetic tumors is
comparable to that of real tumors. More importantly, our method shows a much
higher detection rate for small tumors. We further investigate the per-voxel
segmentation performance of pancreatic tumors if AI is trained on a combination
of CT scans with synthetic tumors and CT scans with annotated large tumors at
an advanced stage. Finally, we show that synthetic tumors improve AI
generalizability in tumor detection and localization when processing CT scans
from different hospitals. Overall, our proposed tumor synthesis method has
immense potential to improve the early detection of pancreatic cancer, leading
to better patient outcomes.