LVC-Net: Medical Image Segmentation with Noisy Label Based on Local Visual Cues
CNN-based deep architecture has been successfully applied to medical image semantic segmentation task because of its effective feature learning mechanism. However, due to the lack of semantic guidance, such supervised learning model may be susceptible to annotation noise. In order to address this problem, we propose a novel medical image segmentation algorithm based on automatic label error correction. Firstly, local visual saliency regions, namely the Local Visual Cues (LVCs), are captured from low-level feature channels. Then, a deformable spatial transformation module is integrated into our LVC-Net to build visual connections between the predictions and LVCs. By combining noisy labels with image LVCs, a novel loss function is proposed based on their intrinsic spatial relationship. Our method can effectively suppress the influence of label noise by utilizing potential visual guidance during the learning process, thereby generate better semantic segmentation results. Comparative experiment on hip x-ray image segmentation task demonstrate that our algorithm achieves significant improvement over state-of-the-arts in the presences of noisy label.
This research was funded in part by National Key R&D Program of China 2016YFC1000307-3, National Natural Science Foundation of China 61801068 & 61906024, Natural Science Foundation of Chongqing cstc2016jcyjA0407, Scientific and Technological Research Program of Chongqing Education Commission KJ1600419. The authors would like to thank Prof. Guoxin Nan for providing the data.
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