Region-Manipulated Fusion Networks for Pancreatitis Recognition
This work addresses the challenge of fine-grained and non-rigid appearance variability in pancreatitis recognition for medical imaging, representing an incremental advancement in applying neural networks to this specific domain.
The authors tackled the problem of automatically recognizing pancreatitis in CT scan images by proposing a Region-Manipulated Fusion Networks (RMFN) to capture local lesion characteristics, and experimental results demonstrated its effectiveness on a real CT image database.
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.