In many real-world applications, learning a binary classifier relying solely on positive and unlabeled (PU) training sample data is an important and challenging task. The absence of negative training samples leads to the necessity for PU learning methods to extract effective additional information from unlabeled training samples. This paper proposes a method that combines Mean Teacher and Smooth Neighbors on Teacher Graphs (SNTG) to address the issue of model overfitting towards unlabeled data in PU learning, where unlabeled data is typically assigned a lower weight. The use of the Mean Teacher model ensures effective utilization of unlabeled data information. SNTG maps the data distribution from high-dimensional space to low-dimensional manifolds or clustering structures, ensuring that the model captures the complex local structures between data points. Moreover, when using the Mean Teacher for data augmentation of unlabeled data points, the intrinsic relationships between the data points are often overlooked, and SNTG helps to address this limitation. By combining the SNTG loss, consistency loss in the Mean Teacher, and non-negative PU loss, the effectiveness of the proposed framework is validated through experiments on three benchmark datasets (MNIST, Fashion-MNIST, CIFAR-10) and two real-world datasets (Avila, EGSS—Electrical Grid Stability Simulated). The results show accuracies of 95.03%, 95.18%, 89.73%, 81.12%, and 92.55%, surpassing the performance of most state-of-the-art PU learning algorithms.

The chip industry is essential for national security and economic development, with integrated circuit (IC) reverse engineering playing a vital role in analyzing chip structures. This process involves several steps, including layer-by-layer image acquisition using scanning electron microscopy (SEM), device identification, gate net extraction, and function inference. Segmenting electrical components and metal lines from IC images is crucial for these analyses. However, traditional image segmentation methods often fail to handle the complex and variable conditions of IC images due to insufficient expert knowledge. This study introduces an improved approach, using the UNet ++ architecture and effentnet-b7 as the encoder, called the E- UNet++ model. A post-processing denoising stage is added that contains Hough circle detection and median filtering for extracting metal lines and perforations in IC images. The primary contributions of this method are: (1) it enables fully automatic detection of metal lines and vias without manual intervention, and (2) it combines E-UNet++, Hough circle detection, and median filtering in a hybrid approach to accurately locate metal lines and vias. Experimental results on over ten thousand IC images, each measuring 1024×1024 and provided by a company, show that training with just 393 images allows the E-UNet++ model to effectively segment metal lines and vias. The average intersection over union (mIoU) is 98.09% and the mean pixel accuracy (MPA) is 99.06%, surpassing the performance of existing methods.

The accurate gland segmentation from digitized H&E (hematoxylin and eosin) histology images with a wide range of histologic grades of cancer is quite challenging. In recent years, several fully convolutional network methods have been proposed, with UNet being the most classic. The UNet model, with its symmetric structure, has shown excellent performance in gland segmentation tasks. However, the locality of convolution operations in UNet also limits its ability to capture global dependencies. To address this limitation, this paper proposes a novel deep glandular tissue image segmentation network based on Swin UNet, termed EMA-Swin UNet. This network replaces CNN modules with Swin Transformer modules to capture both local and global representations. Additionally, the EMA-Swin UNet incorporates an Efficient Multi-scale Attention (EMA) module to enhance multi-scale feature extraction for glandular tissues of various sizes by capturing global dynamic features and long-range smooth features from the encoder outputs. By integrating edge-detection pooling, we enhanced the refinement of prediction maps produced by the EMA-Swin UNet. Moreover, we standardized the staining across both the ClaS dataset and the six-grade tumor differentiation dataset from EBHI-Seg using Reinhard normalization. The final segmentation results are compared with those of classical gland segmentation algorithms on the ClaS and EBHI-Seg datasets, demonstrating the effectiveness of our proposed method. Particularly, on the GlaS dataset, the mDice reached 0.894.