The precise segmentation of the glomerular basement membrane (GBM) can aid pathologists in making accurate pathological diagnoses. However, conventional methods solely focus on segmenting GBM from the background, disregarding the interconnections between GBM and its similar surrounding tissues, which leads to imprecise boundary segmentation of GBM. To address this issue, we employed a multi-category segmentation method to model the distinctions and interconnections between GBM and its similar surrounding tissues. Our experimental results demonstrate that this method can more accurately segment GBM with blurred boundaries. Historically, scholars have primarily used convolution to build models. This approach has limitation that only local information is modeled without effectively extracting global information. To address this issue, we propose a more reasonable structure combining convolution and attention, which we call the Self-Reinforcing Attention Mechanism. Experimental results indicate that the addition of the attention mechanism can help the segmentation of GBM by yielding more continuous boundaries. Finally, we incorporate the feature maps of each layer of the model in the loss function, allowing the model to focus semantic information at varying scales while also providing control over the model’s focus by adjusting the weight. Our experimental results demonstrated that the proposed method has higher performance and better generalization ability than the state-of-the-art approaches.

Sunspots play a crucial role in both weather forecasting and the monitoring of solar storms. In this work, we propose a novel combined model for sunspot prediction using improved gated recurrent units (GRU) guided by pinball loss for probabilistic forecasts. Specifically, we optimize the GRU parameters using the slime mould algorithm and employ a seasonal-trend decomposition procedure based on loess to tackle challenges related to sequence prediction, such as self-correlations and non-stationarity. To address prediction uncertainty, we replace the traditional $$l_2$$l 2 -norm loss with pinball loss. This modification extends the conventional GRU-based point forecasting to a probabilistic framework expressed as quantiles. We apply our proposed model to analyze a well-established historical sunspot dataset for both single- and multi-step ahead forecasting. The results demonstrate the effectiveness of our combined model in predicting sunspot values, surpassing the performance of other existing methods.

Point set registration aims to find a spatial transformation that best aligns two point sets. Algorithms which can handle partial overlap and are invariant to the corresponding transformations are particularly desirable. To this end, we first reduce the objective of the robust point matching (RPM) algorithm to a function of a low dimensional variable. The resulting function is nevertheless only concave over a finite region including the feasible region, which prohibits the use of the popular branch-and-bound (BnB) algorithm. To address this issue, we propose to use the polyhedral annexation (PA) algorithm for optimization, which enjoys the merit of only operating within the concavity region of the objective function. The proposed algorithm does not need regularization on transformation and thus is invariant to the corresponding transformation. It is also approximately globally optimal and thus is guaranteed to be robust. Moreover, its most computationally expensive subroutine is a linear assignment problem which can be efficiently solved. Experimental results demonstrate better robustness of the proposed method over the state-of-the-art algorithms. Our method’s matching error is on average 44% (resp. 65%) lower than that of Go-ICP in 2D (resp. 3D) synthesized tests. It is also efficient when the number of transformation parameters is small.