FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation

With the increase in available large clinical and experimental datasets, there has been substantial amount of work being done on addressing the challenges in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has especially attracted attention... Recent hardware advancement has led to the success of deep learning approaches. However, although deep learning models are being trained on large datasets, existing methods do not use the information from different learning epochs effectively. In this work, we leverage the information of each training epoch to prune the prediction maps of the subsequent epochs. We propose a novel architecture called feedback attention network (FANet) that unifies the previous epoch mask with the feature map of the current training epoch. The previous epoch mask is then used to provide a hard attention to the learnt feature maps at different convolutional layers. The network also allows to rectify the predictions in an iterative fashion during the test time. We show that our proposed feedback attention model provides a substantial improvement on most segmentation metrics tested on seven publicly available biomedical imaging datasets demonstrating the effectiveness of the proposed FANet. read more

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Medical Image Segmentation 2018 Data Science Bowl FANet Dice 0.9176 # 3
mIoU 0.8569 # 2
Recall 0.9222 # 1
Precision 0.9194 # 2
Medical Image Segmentation CHASE_DB1 FANet DSC 0.8108 # 1
Medical Image Segmentation CVC-ClinicDB FANet mean Dice 0.9355 # 1
Medical Image Segmentation EM FANet IoU 0.9134 # 2
DSC 0.9547 # 1
Recall 0.9568 # 1
Specificity 0.8096 # 1
Precision 0.9529 # 1
Medical Image Segmentation ISIC 2018 FANet DSC 0.8731 # 1
Medical Image Segmentation Kvasir-SEG FANet Average MAE 0.8153 # 7
mean Dice 0.8803 # 8


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