  As Deep Convolutional Neural Networks (DCNNs) have shown robust performance
and results in medical image analysis, a number of deep-learning-based tumor
detection methods were developed in recent years. Nowadays, the automatic
detection of pancreatic tumors using contrast-enhanced Computed Tomography (CT)
is widely applied for the diagnosis and staging of pancreatic cancer.
Traditional hand-crafted methods only extract low-level features. Normal
convolutional neural networks, however, fail to make full use of effective
context information, which causes inferior detection results. In this paper, a
novel and efficient pancreatic tumor detection framework aiming at fully
exploiting the context information at multiple scales is designed. More
specifically, the contribution of the proposed method mainly consists of three
components: Augmented Feature Pyramid networks, Self-adaptive Feature Fusion
and a Dependencies Computation (DC) Module. A bottom-up path augmentation to
fully extract and propagate low-level accurate localization information is
established firstly. Then, the Self-adaptive Feature Fusion can encode much
richer context information at multiple scales based on the proposed regions.
Finally, the DC Module is specifically designed to capture the interaction
information between proposals and surrounding tissues. Experimental results
achieve competitive performance in detection with the AUC of 0.9455, which
outperforms other state-of-the-art methods to our best of knowledge,
demonstrating the proposed framework can detect the tumor of pancreatic cancer
efficiently and accurately.
