• We propose a weakly-supervised segmentation method for IHC staining pathological cell images, which only uses positive cell counting labels. • It transforms image level counting results into superpixel level classification results as coarse segmentation masks via the designed adaptive top-k instance selection strategy. • It further generates refined pixel-wise cell segmentation masks via the proposed superpixel mask refining approach based on IHC prior knowledge. • Experiments on two public datasets show that our method performs excellently in cell counting and segmentation tasks, and keeps strong generalization. Immunohistochemistry (IHC) can specifically stain immune cells, and quantitative analysis of tumor-infiltrating immune cells is crucial for the diagnosis of malignant tumors and the prognosis of immunotherapy. However, for current deep learning-based immuno-quantitative methods, it is difficult to obtain labeled data for fine-grained analysis tasks, such as cell segmentation, because manual labeling is laborious. Moreover, information contained in coarse-grained image-level labels, such as positive cell counting labels, is difficult to leverage. Here, we propose a novel multi-stage automated immuno-quantitative analysis model for cell counting and segmentation, which is solely supervised by positive cell counting labels. In order to excavate the potential information of counting labels for cell segmentation, based on the idea of Multiple Instance Learning (MIL), we transform entire image (bag) level counting results into superpixel (instance) level classification results as coarse segmentation masks, using a specifically designed adaptive top-k instance selection strategy. Furthermore, a mask refinement approach is proposed, which can refine coarse masks generated by superpixel instance classification into pixel-level pseudo masks as supervision of the segmentation network. Our method enables all-in-one immuno-quantitative analysis of cell counting and segmentation with only positive cell counting labels, alleviating the dependence on fine-grained labels and economizing labeling cost. Our work is the first to apply MIL to small-scale IHC-stained cell image segmentation task. We trained and tested our model on the public LYSTO dataset and conducted generalization tests on the NuClick IHC dataset. Our model achieved segmentation dice scores of 0.809 and 0.711, respectively, demonstrating approximately 7.44% and 3.95% performance improvements over the state-of-the-art (SOTA) methods. The experiments show that our model performs excellently in cell counting and cell segmentation tasks while maintaining strong generalization capabilities.

Recent research showcases the considerable potential of conditional diffusion models for generating consistent stories. However, current methods, which primarily generate stories in a caption-dependent manner, often overlook the importance of contextual consistency and the relevance of frames during sequential generation. To address this, we propose a novel Rich-contextual Conditional Diffusion Models (RCDMs), a two-stage approach designed to enhance story generation's semantic consistency and temporal consistency. Specifically, in the first stage, the frame-prior transformer diffusion model is presented to predict the frame semantic embedding of the unknown clip by aligning the semantic correlations between the captions and frames of the known clip. The second stage establishes a robust model with rich contextual conditions, including reference images of the known clip, the predicted frame semantic embedding of the unknown clip, and text embeddings of all captions. By jointly injecting these rich contextual conditions at the image and feature levels, RCDMs can generate semantic and temporal consistency stories. Moreover, RCDMs can generate consistent stories with a single forward inference compared to autoregressive models. Our qualitative and quantitative results demonstrate that our proposed RCDMs outperform in challenging scenarios.

Sensory information recognition is primarily processed through the ventral and dorsal visual pathways in the primate brain visual system, which exhibits layered feature representations bearing a strong resemblance to convolutional neural networks (CNNs), encompassing reconstruction and classification. However, existing studies often treat these pathways as distinct entities, focusing individually on pattern reconstruction or classification tasks, overlooking a key feature of biological neurons, the fundamental units for neural computation of visual sensory information. Addressing these limitations, we introduce a unified framework for sensory information recognition with augmented spikes. By integrating pattern reconstruction and classification within a single framework, our approach not only accurately reconstructs multimodal sensory information but also provides precise classification through definitive labeling. Experimental evaluations conducted on various datasets including video scenes, static images, dynamic auditory scenes, and functional magnetic resonance imaging (fMRI) brain activities demonstrate that our framework delivers state-of-the-art pattern reconstruction quality and classification accuracy. The proposed framework enhances the biological realism of multimodal pattern recognition models, offering insights into how the primate brain visual system effectively accomplishes the reconstruction and classification tasks through the integration of ventral and dorsal pathways.

We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzhen (China). The competition required participants to automatically assess the number of lymphocytes, in particular T-cells, in images of colon, breast, and prostate cancer stained with CD3 and CD8 immunohistochemistry. Differently from other challenges setup in medical image analysis, LYSTO participants were solely given a few hours to address this problem. In this paper, we describe the goal and the multi-phase organization of the hackathon; we describe the proposed methods and the on-site results. Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists. We show that some of the participants were capable to achieve pathologist-level performance at lymphocyte assessment. After the hackathon, LYSTO was left as a lightweight plug-and-play benchmark dataset on grand-challenge website, together with an automatic evaluation platform.

Biomedical microscopy images with high-resolution (HR) and axial information can help analysis and diagnosis. However, obtaining such images usually takes more time and economic costs, which makes it impractical in most scenarios. In this paper, we first propose a novel Self-texture Transfer Super-resolution and Refocusing Network (STSRNet) to reconstruct HR multi-focal plane (MFP) images from a single 2D low-resolution (LR) wide field image without relying on scanning or any special devices. The proposed STSRNet consists of three parts: the backbone module for extracting features, the self-texture transfer module for transferring and fusing features, and the flexible reconstruction module for SR and refocusing. Specifically, the self-texture transfer module is designed for images with self-similarity such as cytological images and it searches for similar textures within the image and transfers to help MFP reconstruction. As for reconstruction module, it is composed of multiple pluggable components, each of which is responsible for a specific focal plane, so as to performs SR and refocusing all focal planes at one time to reduce computation. We conduct extensive experiments on cytological images and the experiments show that MFP images reconstructed by STSRNet have richer details in the axial and horizontal directions than input images. At the same time, the reconstructed MFP images also perform better than single 2D wide field images on high-level tasks. The proposed method provides relatively high-quality MFP images when real MFP images cannot be obtained, which greatly expands the application potential of LR wide-field images. To further promote the development of this field, we released our cytology dataset named RSDC for more researchers to use.

In the cytopathology screening of cervical cancer, high-resolution digital cytopathological slides are critical for the interpretation of lesion cells. However, the acquisition of high-resolution digital slides requires high-end imaging equipment and long scanning time. In the study, we propose a GAN-based progressive multi-supervised super-resolution model called PathSRGAN (pathology super-resolution GAN) to learn the mapping of real low-resolution and high-resolution cytopathological images. With respect to the characteristics of cytopathological images, we design a new two-stage generator architecture with two supervision terms. The generator of the first stage corresponds to a densely-connected U-Net and achieves 4× to 10× super resolution. The generator of the second stage corresponds to a residual-in-residual DenseBlock and achieves 10× to 20× super resolution. The designed generator alleviates the difficulty in learning the mapping from 4× images to 20× images caused by the great numerical aperture difference and generates high quality high-resolution images. We conduct a series of comparison experiments and demonstrate the superiority of PathSRGAN to mainstream CNN-based and GAN-based super-resolution methods in cytopathological images. Simultaneously, the reconstructed high-resolution images by PathSRGAN improve the accuracy of computer-aided diagnosis tasks effectively. It is anticipated that the study will help increase the penetration rate of cytopathology screening in remote and impoverished areas that lack high-end imaging equipment.

Trust management is an important issue in wireless sensor networks (WSNs). In this paper, we introduce the concept of trust and design a distributed event- triggered trust management model for WSNs. It combines cryptography, statistics, economics, data analysis and the other related fields. In this way, we can immediately identify and isolate the malice node, thereby creating a secure and reliable wireless sensor networks that can avoid some common attacks and ensure the security of the applications. And, we give the communication process and the computation model as well as how each trust management module works. So it is no longer a simple calculation model or a simple frame structure, and it has realistic feasibility and operability.