Self-training is a strong baseline for semi-supervised domain adaptive semantic segmentation. However, it inevitably introduces biased links between features and concepts in the prediction of certain "hard pixels", which may mislead the generalization of models. We consider these hard pixels to come from two aspects: First, the labels are severely imbalanced and distributed across domains and classes, which may lead to features biased towards source domains and majority classes. Second, the naive threshold filtering pseudo-label methods limit the supervision of hard pixels. To address the above problems, we propose a novel co-training framework with progressive distribution alignment and uncertainty-interactive relabeling strategies. More concretely, a progressive distribution alignment strategy is proposed to match distribution across domains while providing additional supervision for tail class pixels. Additionally, an uncertainty-interactive relabeling strategy is proposed to retain more supervisory information for hard pixels and reduce the overall uncertainty of the pseudo-labels. Experiments on two widely-used benchmarks demonstrate the effectiveness of the proposed PDAUR, achieving state-of-the-art results.

The resolution of a whole slide image (WSI) is too large to process directly, but WSI can be segmented into patches and be classified through multiple instance learning (MIL). Some patches have either close distances or similar pathological morphology, indicating that there are at least two types of relationships between patches. However, the existing MIL methods often deal with instance relationships simplistically. To solve this problem, we propose a new model named DualGCN-MIL. It analyzes the two types of relationships in WSI by constructing two different graphs in high-dimensional feature space and physical coordinate space and uses graph convolutional networks with unshared parameters for learning. Various experiments conducted on the Camelyon dataset have shown that compared to state-of-the-art methods, we can achieve better performance in multiple indicators, with accuracy of 88.89% and 85.11% on the 16 and 17 datasets, respectively, which proves that DualGCN-MIL has a higher accuracy in WSI classification. Implementation can be obtained from https://github.com/UnmatchedKatana/DualGCN-MIL.

The class imbalance problem, which is prevalent in medical image datasets, seriously affects the diagnostic effectiveness of deep learning-based network models. To alleviate this problem, data re-sampling and loss re-weighting techniques are often used to reshape the decision boundary of the classifier. However, these techniques still lead to biased decision boundary due to the lack of sufficient and diversified samples in the tail classes of medical image datasets. In this paper, we propose an implicit feature augmentation with feature transfer (FT-IFA) method which solves the class imbalance problem by expanding the feature space of tail classes to reshape the decision boundary of the classifier. FT-IFA utilizes prototype similarity to transfer the rich transformation information from the head classes to tail classes on the basis of the balanced feature space, enriching the intra-class diversity of tail classes. Experimental results on two class-imbalanced medical image datasets show that FT-IFA outperforms the current state-of-the-art methods and effectively solves the class imbalance problem.

MRI technology is usually used to distinguish the grade of the tumor in the patient. Due to technical limitations, the classification of tumors (high-grade gliomas and metastases) on MRI images has become a problem for doctors. At present, the widely used neural network is gradually applied to the tumor classification of MRI images, which not only reduces the burden of human resources, but also shows good classification accuracy. Although different good experimental data are obtained under various neural networks, there is still a problem in using these neural networks for tumor classification: the semantic information expressed on the image by the deep features of the neural network is too scattered, and it is difficult to concentrate the lesion area. In this article, we propose a new strategy that combines the approximate symmetry properties of the MRI image with neural network, then uses a dual-branch network instead of the basic network for feature extraction, and adds complementary learning to the network, different features fusion and attention mechanism to enrich detailed information. Our method performs multiple comparison and ablation experiments on the dataset of glioma and metastasis, which proves that the proposed method is effective for tumor classification assisted by MRI.