Lesion Classification of Wireless Capsule Endoscopy Images

In this paper, we propose a scheme to classify different Wireless Capsule Endoscopy (WCE) lesion images for diagnosis. The main contribution is to quantify multi-scale pooled channel-wise information and merge multi-level features together by explicitly modeling interdependencies between all feature maps of different convolution layers. Firstly, feature maps are resized into multi-scale size with bicubic interpolation, and then down-sampling convolution method is adopted to obtain pooled feature maps of the same resolution, and finally one by one convolution kernels are utilized to fuse feature maps after quantization operation based on channel-wise attention mechanism in order to enhance feature extraction of the proposed architecture. Preliminary experimental result shows that our proposed scheme with less model parameters achieves competitive results compared to the state-of-the-art methods in WCE image classification task.