DRBANET: A Lightweight Dual-Resolution Network For Semantic Segmentation With Boundary Auxiliary

The success of modern deep learning algorithms for image segmentation heavily relies on the availability of high-quality labels for training. However, obtaining accurate labels is time-consuming and tedious, and requires expertise. If directly trained with dataset with noisy annotations, networks can easily overfit to noisy labels and result in poor performance, which might lead to serious misinterpretation. To this end, we propose a noisy pixel estimation approach based on deep neural network, which helps correct the noisy annotations resulting in better prediction performance. First, a deep neural network is trained to detect noisy pixels from image annotations. Then, the estimated noisy pixels are used to correct the noisy annotations. Finally, the corrected annotations are used to train the deep learning model. Our proposed framework is validated on the breast tumor segmentation task. The obtained experimental results show that our proposed method can improve the robustness of deep learning model under noisy annotations while achieving favorable performance against existing noisy label correction methods.