Cervical cancer is the fourth most common cancer in women and its subtyping requires examining histopathological slides or digital images, such as whole slide images (WSIs). However, manually inspecting WSIs with gigapixel sizes can be laborious and prone to errors for pathologists. To address this issue, computer-aided approaches based on weakly-supervised learning techniques have been proposed. These methods can predict disease types directly from WSIs and highlight diagnosis-relevant regions, which can help pathologists achieve faster and more accurate diagnoses. WSIs are divided into overlapping patches using a sliding window approach, and these patches are subsequently screened in a sequential zig-zag pattern to identify spatiotemporal dependencies. These dependencies are further analyzed to generate predictions at the WSI level. Therefore, effective patch feature learning and spatiotemporal aggregation are two key issues in the weakly-supervised WSI classification (WSWC) task. In this paper, we present a label-efficient WSWC method called spatiotemporal aggregation for cervical WSIs (SAC-Net), which jointly performs online feature extraction and feature aggregation to infer the WSI-level prediction in an end-to-end manner. The online feature extractor helps to learn cervical-cancer-specific features and obtain more accurate patch representations. The feature aggregator uses an online instance clustering method to learn proper weight parameters for each cluster, which generates the WSI embedding with enhanced spatiotemporal aggregation. SAC-Net is developed and evaluated on a public cervical WSI dataset (TissueNet) containing 1015 WSIs, which are also externally tested on three independent cervical WSI datasets. Our results demonstrate that SAC-Net achieves state-of-the-art classification performance and is robust. SAC-Net has the potential to be a useful tool for clinical cervical cancer detection.

The advancement of computer vision has pushed visual analysis tasks from still images to the video domain. In recent years, video instance segmentation, which aims at tracking and segment multiple objects in video frames, has drawn much attention for its potential applications in various emerging areas such as autonomous driving, intelligent transportation, and smart retail. In this article, we propose an effective framework for instance-level visual analysis on video frames, which can simultaneously conduct object detection, instance segmentation, and multi-object tracking. The core idea of our method is collaborative multi-task learning which is achieved by a novel structure, named associative connections among detection, segmentation, and tracking task heads in an end-to-end learnable CNN. These additional connections allow information propagation across multiple related tasks, so as to benefit these tasks simultaneously. We evaluate the proposed method extensively on KITTI MOTS and MOTS Challenge datasets and obtain quite encouraging results.

Inspired by neuronal diversity in the biological neural system, a plethora of studies proposed to design novel types of artificial neurons and introduce neuronal diversity into artificial neural networks. Recently proposed quadratic neuron, which replaces the inner-product operation in conventional neurons with a quadratic one, have achieved great success in many essential tasks. Despite the promising results of quadratic neurons, there is still an unresolved issue: Is the superior performance of quadratic networks simply due to the increased parameters or due to the intrinsic expressive capability? Without clarifying this issue, the performance of quadratic networks is always suspicious. Additionally, resolving this issue is reduced to finding killer applications of quadratic networks. In this paper, with theoretical and empirical studies, we show that quadratic networks enjoy parametric efficiency, thereby confirming that the superior performance of quadratic networks is due to the intrinsic expressive capability. This intrinsic expressive ability comes from that quadratic neurons can easily represent nonlinear interaction, while it is hard for conventional neurons. Theoretically, we derive the approximation efficiency of quadratic networks over conventional ones in terms of real space and manifolds. Moreover, from the perspective of the Barron space, we demonstrate that there exists a functional space whose functions can be approximated by quadratic networks in a dimension-free error, but the approximation error of conventional networks is dependent on dimensions. Empirically, experimental results on synthetic data, classic benchmarks, and real-world applications show that quadratic models broadly enjoy parametric efficiency, and the gain of efficiency depends on the task.

Spatial transcriptomics (ST) has become an important methodology in the analysis of the tumor microenvironment (TME) due to its ability to provide gene expression information with spatial resolution, enabling the identification and characterization of TME gene markers. Deep learning methods are proposed for analyzing spatial transcriptomic data for clustering the spatial regions of the TME based on gene expression. However, deep learning methods are often imposed by errors, which can impact the accuracy of gene expression quantification and TME gene identification. To address this issue, we propose a label-efficient method that utilizes curriculum learning and confidence learning to identify errors in graph deep learning when analyzing ST data. Our method explicitly incorporates the effect of noise in the learning process and employs probabilistic models or uncertainty estimates to represent the uncertainty in the data. Validated on human breast cancer ST data, we studied spatial gene expression in HER2-positive breast tumors using our method. The evaluation results suggest that the error quantification helps identify the noisy samples and subset the samples that results in more accurate gene expression quantification and TME gene identification. Additionally, there are biological insights obtained from the new subset formed by error samples. This error-robust deep learning method offers promising avenues for the analysis of spatial transcriptomic data, enabling accurate and label-efficient quantification of gene expression and identification of TME gene markers.

Video instance segmentation (VIS) is a new and critical task in computer vision. To date, top-performing VIS methods extend the two-stage Mask R-CNN by adding a tracking branch, leaving plenty of room for improvement. In contrast, we approach the VIS task from a new perspective and propose a one-stage spatial granularity network (SG-Net). Compared to the conventional two-stage methods, SG-Net demonstrates four advantages: 1) Our method has a one-stage compact architecture and each task head (detection, segmentation, and tracking) is crafted interdependently so they can effectively share features and enjoy the joint optimization; 2) Our mask prediction is dynamically performed on the sub-regions of each detected instance, leading to high-quality masks of fine granularity; 3) Each of our task predictions avoids using expensive proposal-based RoI features, resulting in much reduced runtime complexity per instance; 4) Our tracking head models objects centerness movements for tracking, which effectively enhances the tracking robustness to different object appearances. In evaluation, we present state-of-the-art comparisons on the YouTube-VIS dataset. Extensive experiments demonstrate that our compact one-stage method can achieve improved performance in both accuracy and inference speed. We hope our SG-Net could serve as a strong and flexible baseline for the VIS task. Our code will be available.