Taming Detection Transformers for Medical Object Detection
Marc K. Ickler Michael Baumgartner Saikat Roy Tassilo Wald Klaus H. Maier-Hein
The accurate detection of suspicious regions in medical images is an
error-prone and time-consuming process required by many routinely performed
diagnostic procedures. To support clinicians during this difficult task,
several automated solutions were proposed relying on complex methods with many
hyperparameters. In this study, we investigate the feasibility of DEtection
TRansformer (DETR) models for volumetric medical object detection. In contrast
to previous works, these models directly predict a set of objects without
relying on the design of anchors or manual heuristics such as
non-maximum-suppression to detect objects. We show by conducting extensive
experiments with three models, namely DETR, Conditional DETR, and DINO DETR on
four data sets (CADA, RibFrac, KiTS19, and LIDC) that these set prediction
models can perform on par with or even better than currently existing methods.
DINO DETR, the best-performing model in our experiments demonstrates this by
outperforming a strong anchor-based one-stage detector, Retina U-Net, on three
out of four data sets.