Brain tumor segmentation is a key step in brain cancer diagnosis. Segmentation of brain tumor sub-regions, including necrotic, enhancing, and edematous regions, can provide more detailed guidance for clinical diagnosis. Weakly supervised brain tumor segmentation methods have received much attention because they do not require time-consuming pixel-level annotations. However, existing weakly supervised methods focus on the segmentation of the entire tumor region while ignoring the challenging task of multi-label segmentation for the tumor sub-regions. In this paper, we propose a weakly supervised approach to solve the multi-label brain tumor segmentation problem. To the best of our knowledge, it's the first end-to-end multi-label weakly supervised segmentation model applied to brain tumor segmentation. With well-designed loss functions and a contrastive learning pre-training process, our proposed Transformer-based segmentation method (WS-MTST) has the ability to perform segmentation of brain tumor sub-regions. We conduct comprehensive experiments and demonstrate that our method reaches the state-of-the-art on the popular brain tumor dataset BraTS (from 2018 to 2020).

Image analysis can play an important role in supporting histopathological diagnoses of lung cancer, with deep learning methods already achieving remarkable results. However, due to the large scale of whole-slide images (WSIs), creating manual pixel-wise annotations from expert pathologists is expensive and time-consuming. In addition, the heterogeneity of tumors and similarities in the morphological phenotype of tumor subtypes have caused inter-observer variability in annotations, which limits optimal performance. Effective use of weak labels could potentially alleviate these issues. In this paper, we propose a two-stage transformer-based weakly supervised learning framework called Simple Shuffle-Remix Vision Transformer (SSRViT). Firstly, we introduce a Shuffle-Remix Vision Transformer (SRViT) to retrieve discriminative local tokens and extract effective representative features. Then, the token features are selected and aggregated to generate sparse representations of WSIs, which are fed into a simple transformer-based classifier (SViT) for slide-level prediction. Experimental results demonstrate that the performance of our proposed SSRViT is significantly improved compared with other state-of-the-art methods in discriminating between adenocarcinoma, pulmonary sclerosing pneumocytoma and normal lung tissue (accuracy of 96.9% and AUC of 99.6%).

Nuclei segmentation is an essential step in the automatic analysis of histopathology images. This segmentation task requires much work to manually generate accurate pixel-level annotations for fully supervised training. To overcome such expensive and hard-to-get pixel-level annotations, in this paper we propose a weakly supervised method for nuclei segmentation only using point annotations based on convolutional neural network. The proposed method effectively combines a weakly supervised segmentation task and an auxiliary colorization task. The dual input with boundaries and color information maximizes the inherent features of the image. Two types of coarse labels generated from point annotations are applied to provide constraint information for the segmentation task. As an auxiliary task, colorization is incorporated to guide the network to extract effective features and improve the segmentation performance of the network. We evaluate our proposed method on two public nuclei segmentation datasets. The experimental results indicate that our method is superior to other state-of-the-art methods.

Multivariate time series (MTS) anomaly detection is of great importance in both condition monitoring and malfunction identification within multi-sensor systems. Current MTS anomaly detection approaches are typically based on reconstruction, prediction or association discrepancy learning algorithms. These methods detect anomalies by learning hidden representations of entire sequences, modeling dependencies at a single time-step level, or calculating an association-based metric inherently distinguishable between regular and deviant points. However, most existing methods typically fail to leverage all three types of models simultaneously to enhance overall performance, as well as often disregard the correlations between different sensors. To address the issues above, this paper proposes a novel deep learning-based unsupervised MTS anomaly detection algorithm called Association Discrepancy Dual-decoder Transformer (AD2T). AD2T employs a dual-decoder architecture to accommodate reconstruction, prediction, and association discrepancy learning tasks, thereby effectively utilizing information across these tasks to better characterize MTS data. We further develop a minmax training strategy to jointly optimize all the aforementioned tasks. Additionally, we propose a compound embedding module based on dilated causal convolution to simultaneously capture correlations in both temporal and sensor dimensions. Extensive empirical studies on five multi-sensor system datasets from the aerospace, server, and water treatment domains have demonstrated the superiority of our method, achieving an average improvement of 1.96% in F1-score compared to state-of-the-art (SOTA) methods.