  Incorporating data-specific domain knowledge in deep networks explicitly can
provide important cues beneficial for lesion detection and can mitigate the
need for diverse heterogeneous datasets for learning robust detectors. In this
paper, we exploit the domain information present in computed tomography (CT)
scans and propose a robust universal lesion detection (ULD) network that can
detect lesions across all organs of the body by training on a single dataset,
DeepLesion. We analyze CT-slices of varying intensities, generated using
heuristically determined Hounsfield Unit(HU) windows that individually
highlight different organs and are given as inputs to the deep network. The
features obtained from the multiple intensity images are fused using a novel
convolution augmented multi-head self-attention module and subsequently, passed
to a Region Proposal Network (RPN) for lesion detection. In addition, we
observed that traditional anchor boxes used in RPN for natural images are not
suitable for lesion sizes often found in medical images. Therefore, we propose
to use lesion-specific anchor sizes and ratios in the RPN for improving the
detection performance. We use self-supervision to initialize weights of our
network on the DeepLesion dataset to further imbibe domain knowledge. Our
proposed Domain Knowledge augmented Multi-head Attention based Universal Lesion
Detection Network DMKA-ULD produces refined and precise bounding boxes around
lesions across different organs. We evaluate the efficacy of our network on the
publicly available DeepLesion dataset which comprises of approximately 32K CT
scans with annotated lesions across all organs of the body. Results demonstrate
that we outperform existing state-of-the-art methods achieving an overall
sensitivity of 87.16%.
