  This work first attempts to automatically recognize pancreatitis on CT scan
images. However, different form the traditional object recognition, such
pancreatitis recognition is challenging due to the fine-grained and non-rigid
appearance variability of the local diseased regions. To this end, we propose a
customized Region-Manipulated Fusion Networks (RMFN) to capture the key
characteristics of local lesion for pancreatitis recognition. Specifically, to
effectively highlight the imperceptible lesion regions, a novel
region-manipulated scheme in RMFN is proposed to force the lesion regions while
weaken the non-lesion regions by ceaselessly aggregating the multi-scale local
information onto feature maps. The proposed scheme can be flexibly equipped
into the existing neural networks, such as AlexNet and VGG. To evaluate the
performance of the propose method, a real CT image database about pancreatitis
is collected from hospitals \footnote{The database is available later}. And
experimental results on such database well demonstrate the effectiveness of the
proposed method for pancreatitis recognition.
