Adaptive Noise-Tolerant Network for Image Segmentation
This addresses the problem of noisy label training for image segmentation in biomedical domains, offering an incremental improvement by adapting noisy label learning to segmentation tasks.
The paper tackles the challenge of obtaining ground-truth segmentation labels for training deep networks, especially in biomedical images like histopathological images, by proposing an Adaptive Noise-Tolerant Network (ANTN) that integrates multiple noisy segmentation labels adaptively, demonstrating effectiveness and superiority over existing methods on synthetic and real-world data.
Unlike image classification and annotation, for which deep network models have achieved dominating superior performances compared to traditional computer vision algorithms, deep learning for automatic image segmentation still faces critical challenges. One of such hurdles is to obtain ground-truth segmentations as the training labels for deep network training. Especially when we study biomedical images, such as histopathological images (histo-images), it is unrealistic to ask for manual segmentation labels as the ground truth for training due to the fine image resolution as well as the large image size and complexity. In this paper, instead of relying on clean segmentation labels, we study whether and how integrating imperfect or noisy segmentation results from off-the-shelf segmentation algorithms may help achieve better segmentation results through a new Adaptive Noise-Tolerant Network (ANTN) model. We extend the noisy label deep learning to image segmentation with two novel aspects: (1) multiple noisy labels can be integrated into one deep learning model; (2) noisy segmentation modeling, including probabilistic parameters, is adaptive, depending on the given testing image appearance. Implementation of the new ANTN model on both the synthetic data and real-world histo-images demonstrates its effectiveness and superiority over off-the-shelf and other existing deep-learning-based image segmentation algorithms.