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BA-Net: Brightness prior guided attention network for colonic polyp segmentation

Xia Haiying, Qin Yilin, Tan Yumei, Song Shuixiang

Biocybernetics and Biomedical Engineering

2023

Vol. 43, no. 3

603--615

Automatic polyp segmentation at colonoscopy plays an important role in the early diagnosis and surgery of colorectal cancer. However, the diversity of polyps in different images greatly increases the difficulty of accurately segmenting polyps. Manual segmentation of polyps in colonoscopic images is time-consuming and the rate of polyps missed remains high. In this paper, we propose a brightness prior guided attention network (BA-Net) for automatic polyp segmentation. Specifically, we first aggregate the high-level features of the last three layers of the encoder with an enhanced receptive field (ERF) module, which further fed to the decoder to obtain the initial prediction maps. Then, we introduce a brightness prior fusion (BF) module that fuses the brightness prior information into the multi-scale side-out high-level semantic features. The BF module aims to induce the network to localize salient regions, which may be potential polyps, to obtain better segmentation results. Finally, we propose a global reverse attention (GRA) module to combine the output of the BF module and the initial prediction map for obtaining long-range dependence and reverse refinement prediction results. With iterative refinement from higher-level semantics to lower-level semantics, our BA-Net can achieve more refined and accurate segmentation. Extensive experiments show that our BA-Net outperforms the stateof-the-art methods on six common polyp datasets.

Microaneurysm detection in fundus images based on a novel end-to-end convolutional neural network

Liao Yinhan, Xia Haiying, Song Shuxiang, Li Haisheng

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 2

589--604

Microaneurysms are the earliest symptom of diabetic retinopathy and play an important role in the screening of diabetic retinopathy. However, because of the complex background, automatic detection microaneurysm in fundus images is a challenging task. Firstly, motivated by the characteristics of microaneurysm, a novel deep convolutional encoder-decoder network for microaneurysm detection is designed to locate the MAs by the differences between the skip connection in the network. Then, a weighted dice loss, termed the smooth dice loss, is presented to put more focus on misclassified microaneurysms. Finally, an activation function with a long tail is used to produce an accurate probability map for MA detection. Plenty of experiments, conducted on the Retinopathy Online Challenge data-set and the e-ophtha-MA dataset, demonstrate that the proposed model achieves the comparable performance to the existing state-of-the-art methods on microaneurysm detection with only one-hundredth the running time compared with its counterparts. The proposed method is simple and effective, guarantees the performance while shortening the test time. It indicates the potential application in the auxiliary diagnosis of diabetic retinopathy screening.