The Transformer-based methods provide a good opportunity for modeling the global context of gigapixel whole slide image (WSI), however, there are still two main problems in applying Transformer to WSI-based survival analysis task. First, the training data for survival analysis is limited, which makes the model prone to overfitting. This problem is even worse for Transformer-based models which require large-scale data to train. Second, WSI is of extremely high resolution (up to 150,000 x 150,000 pixels) and is typically organized as a multi-resolution pyramid. Vanilla Transformer cannot model the hierarchical structure of WSI (such as patch cluster-level relationships), which makes it incapable of learning hierarchical WSI representation. To address these problems, in this paper, we propose a novel Sparse and Hierarchical Transformer (SH-Transformer) for survival analysis. Specifically, we introduce sparse self-attention to alleviate the overfitting problem, and propose a hierarchical Transformer structure to learn the hierarchical WSI representation. Experimental results based on three WSI datasets show that the proposed framework outperforms the state-of-the-art methods.

Survival analysis is a significant study in cancer prognosis, and the multi-modal data, including histopathological images, genomic data, and clinical information, provides unprecedented opportunities for its development. However, because of the high dimensionality and the heterogeneity of histopathological images and genomic data, acquiring effective predictive characters from these multi-modal data has always been a challenge for survival analysis. In this article, we propose a transformer-based survival analysis model (TransSurv) for colorectal cancer that can effectively integrate intra-modality and inter-modality features of histopathological images, genomic data, and clinical information. Specifically, to integrate the intra-modality relationship of image patches, we develop a multi-scale histopathological features fusion transformer (MS-Trans). Furthermore, we provide a cross-modal fusion transformer based on cross attention for multi-scale pathological representation and multi-omics representation, which includes RNA-seq expression and copy number alteration (CNA). At the output layer of the TransSurv, we adopt the Cox layer to integrate multi-modal fusion representation with clinical information for end-to-end survival analysis. The experimental results on the Cancer Genome Atlas (TCGA) colorectal cancer cohort demonstrate that the proposed TransSurv outperforms the existing methods and improves the prognosis prediction of colorectal cancer.

Early rumor detection is a key challenging task to prevent rumors from spreading widely. Sociological research shows that social bots’ behavior in the early stage has become the main reason for rumors’ wide spread. However, current models do not explicitly distinguish genuine users from social bots, and their failure in identifying rumors timely. Therefore, this paper aims at early rumor detection by accounting for social bots’ behavior, and presents a Social Bot-Aware Graph Neural Network, named SBAG. SBAG firstly pre-trains a multi-layer perception network to capture social bot features, and then constructs multiple graph neural networks by embedding the features to model the early propagation of posts, which is further used to detect rumors. Extensive experiments on three benchmark datasets show that SBAG achieves significant improvements against the baselines and also identifies rumors within 3 hours while maintaining more than 90% accuracy.

Survival analysis is crucial to the evaluation of cancer treatment options and deep learning-based methods integrating pathological images and genomic data have been used for prognosis prediction. However, the most methods are based on the analysis of pathological image patches, thus ignoring the morphological structure information at larger field-of-view and intrinsic relationships between patches. Meanwhile, the existing models fail to exploit the powerful representation learning capabilities of the neural networks for effective multimodal feature fusion of pathological images and genomic data. In this paper, we propose a novel transformer-based fusion network integrating pathological images and genomic data (PGTFNet) for cancer survival analysis. Specifically, we present a transformer-based feature fusion module for multi-scale pathological slides to fully exploit the intra-modality relationships between image patches at various fields of view. Moreover, in order to make effective inter-modality feature fusion of pathological images and genomic data, we introduce a cross-attention transformer module that can exchange feature representations of different modalities between two transformers branches. The PG-TFNet is performed on the colorectal cancer dataset from the Cancer Genome Atlas (TCGA), which contains paired whole-slide images and genomic data with ground truth survival data. The experimental results from a 10-fold cross validation demonstrate that the proposed PG-TFNet facilitates the prognosis prediction of colorectal cancer and shows superiority over the existing methods.

Automatic breast cancer grading methods based on HE stained pathological images can be summarized into two categories. The first category is to use learning-based methods to directly extract the features of the pathological image for breast cancer grading. However, unlike the coarse-grained problem of breast cancer classification, grading of breast Invasive Ductal Carcinoma (IDC) is a fine-grained classification problem. Only using general methods cannot classify IDC well. The second category is to conduct the three evaluation criteria of Nottingham Grading System (NGS) separately, and then integrate the results of the three criteria to obtain the final IDC grading result. However, NGS is only a semi-quantitative evaluation method. The inherent medical motivation of NGS is to grade IDC with the help of nuclei-related features. In this paper, we proposed a nuclei-aware network for IDC grading in pathological images. The entire network achieves an effect similar to the attention mechanism in end-to-end learning, so as to learn fine-grained and nuclei-related feature representations for IDC grading. It should to be pointed out that our method can emphasize custom areas, thus providing a way to model medical knowledge into the network structure. This is different from the general attention mechanism that cannot artificially control the area of attention. Experimental results show that the performance of proposed method is better than the state-of-the-art.

Segmenting the cerebral vessels precisely from the time-of-flight magnetic resonance angiography (TOF-MRA) images is important for the diagnosis and therapy of the cerebrovascular diseases. Since the complex structures of cerebral vessels, the current cerebrovascular segmentation algorithms based on statistical model have less accuracy for stenotic vessels and are quite time-consuming. In this paper, we propose a novel automatic cerebrovascular segmentation algorithm based on focused Multi-Gaussians (FMG) model and weighted 3D Markov Random Field. As far as our knowledge, this is the first time to adopt multi-Gaussians distributions as vascular model with the purpose of modeling the vascular tissue more accurately. Furthermore, the fitting range is narrowed to local region related to vessels in order to make the model focus on the vascular tissue and simplify the finite mixture model. To incorporate precise local character of images to the model, we design a new weighted 3D MRF by a weighted neighborhood system (W-NBS). Finally, the particle swarm optimization (PSO) algorithm of parameter estimation has been implemented parallelly based on GPUs and the execution speed was improved by about 70 times. The experimental results show that the algorithm can produce detailed segmentation results especially for stenotic vessels.