Histopathology image analysis is fundamental to digital pathology, with hematoxylin and eosin (H&E) staining as the gold standard for diagnostic and prognostic assessments. While H&E imaging effectively highlights cellular and tissue structures, it lacks sensitivity to birefringence and tissue anisotropy, which are crucial for assessing collagen organization, fiber alignment, and microstructural alterations-key indicators of tumor progression, fibrosis, and other pathological conditions. To bridge this gap, we construct a polarization imaging system and curate a new dataset of over 13,000 paired Polar-H&E images. Visualizations of polarization properties reveal distinctive optical signatures in pathological tissues, underscoring its diagnostic value. Building on this dataset, we propose PolarHE, a dual-modality fusion framework that integrates H&E with polarization imaging, leveraging the latter's ability to enhance tissue characterization. Our approach employs a feature decomposition strategy to disentangle common and modality-specific features, ensuring effective multimodal representation learning. Through comprehensive validation, our approach significantly outperforms previous methods, achieving an accuracy of 86.70 % on the Chaoyang dataset and 89.06 % on the MHIST dataset. These results demonstrate that polarization imaging is a powerful and underutilized modality in computational pathology, enriching feature representation and improving diagnostic accuracy. PolarHE establishes a promising direction for multimodal learning, paving the way for more interpretable and generalizable pathology models.

The concealment of unexploded ordnance (UXO) left behind by wars and live firing exercises is a problem, not only posing a serious threat to the safety of local residents, but also bringing great difficulties to explosive disposal work. The magnetic detection of UXO has the advantages of portability and efficiency, but it is difficult to recognize the types of UXO through magnetic moment estimation. A deep learning method for recognizing types of UXO in response to the current difficulties in magnetic detection is proposed. By designing a magnetic flux gate array acquisition system and conducting magnetic detection experiments on UXO simulated targets, the effective detection distance of this method is found to be about 2.2 m. The accuracy of recognizing three types of UXO simulated targets is greater than 95.8% and the F1-score is larger than 92.5%. The accuracy is higher than 85.9% and the F1-score is greater than 81.1% under the effects of interference. This method can suppress the influences of environmental magnetic fields, providing a technical reference for recognizing types of UXO based on magnetic detection.

OBJECTIVE: Nuclei segmentation is a crucial pre-task for pathological microenvironment quantification. However, the acquisition of manually precise nuclei annotations for improving the performance of deep learning models is time-consuming and expensive. METHODS: In this paper, an efficient nuclear annotation tool called NuSEA is proposed to achieve accurate nucleus segmentation, where a simple but effective ellipse annotation is applied. Specifically, the core network U-Light of NuSEA is lightweight with only 0.86 M parameters, which is suitable for real-time nuclei segmentation. In addition, an Elliptical Field Loss and a Texture Loss are proposed to enhance the edge segmentation and constrain the smoothness simultaneously. RESULTS: Extensive experiments on three public datasets (MoNuSeg, CPM-17, and CoNSeP) demonstrate that NuSEA is superior to the state-of-the-art (SOTA) methods and better than existing algorithms based on point, rectangle, and text annotations. CONCLUSIONS: With the assistance of NuSEA, a new dataset called NuSEA-dataset v1.0, encompassing 118,857 annotated nuclei from the whole-slide images of 12 organs is released. SIGNIFICANCE: NuSEA provides a rapid and effective annotation tool for nuclei in histopathological images, benefiting future explorations in deep learning algorithms.

Deep learning in digital pathology brings intelligence and automation as substantial enhancements to pathological analysis, the gold standard of clinical diagnosis. However, multiple steps from tissue preparation to slide imaging introduce various image corruptions, making it difficult for deep neural network (DNN) models to achieve stable diagnostic results for clinical use. In order to assess and further enhance the robustness of the models, we analyze the physical causes of the full-stack corruptions throughout the pathological life-cycle and propose an Omni-Corruption Emulation (OmniCE) method to reproduce 21 types of corruptions quantified with 5-level severity. We then construct three OmniCE-corrupted benchmark datasets at both patch level and slide level and assess the robustness of popular DNNs in classification and segmentation tasks. Further, we explore to use the OmniCE-corrupted datasets as augmentation data for training and experiments to verify that the generalization ability of the models has been significantly enhanced.

OBJECTIVE: Cervical cancer, as one of the most frequently diagnosed cancers in women, is curable when detected early. However, automated algorithms for cervical pathology precancerous diagnosis are limited. METHODS: In this paper, instead of popular patch-wise classification, an end-to-end patch-wise segmentation algorithm is proposed to focus on the spatial structure changes of pathological tissues. Specifically, a triple up-sampling segmentation network (TriUpSegNet) is constructed to aggregate spatial information. Second, a distribution consistency loss (DC-loss) is designed to constrain the model to fit the inter-class relationship of the cervix. Third, the Gauss-like weighted post-processing is employed to reduce patch stitching deviation and noise. RESULTS: The algorithm is evaluated on three challenging and public datasets: 1) MTCHI for cervical precancerous diagnosis, 2) DigestPath for colon cancer, and 3) PAIP for liver cancer. The Dice coefficient is 0.7413 on the MTCHI dataset, which is significantly higher than the published state-of-the-art results. CONCLUSION: Experiments on the public dataset MTCHI indicate the superiority of the proposed algorithm on cervical pathology precancerous diagnosis. In addition, the experiments on two other pathological datasets, i.e., DigestPath and PAIP, demonstrate the effectiveness and generalization ability of the TriUpSegNet and weighted post-processing on colon and liver cancers. SIGNIFICANCE: The end-to-end TriUpSegNet with DC-loss and weighted post-processing leads to improved segmentation in pathology of various cancers.

Cervical cancer, as one of the most frequently diagnosed cancers worldwide, is curable when detected early. Histopathology images play an important role in precision medicine of the cervical lesions. However, few computer aided algorithms have been explored on cervical histopathology images due to the lack of public datasets. In this article, we release a new cervical histopathology image dataset for automated precancerous diagnosis. Specifically, 100 slides from 71 patients are annotated by three independent pathologists. To show the difficulty of the task, benchmarks are obtained through both fully and weakly supervised learning. Extensive experiments based on typical classification and semantic segmentation networks are carried out to provide strong baselines. In particular, a strategy of assembling classification, segmentation, and pseudo-labeling is proposed to further improve the performance. The Dice coefficient reaches 0.7833, indicating the feasibility of computer aided diagnosis and the effectiveness of our weakly supervised ensemble algorithm. The dataset and evaluation codes are publicly available. To the best of our knowledge, it is the first public cervical histopathology dataset for automated precancerous segmentation. We believe that this work will attract researchers to explore novel algorithms on cervical automated diagnosis, thereby assisting doctors and patients clinically.

Deep learning has been widely applied in the field of medical image processing. However, compared with flourishing visual tasks in natural images, the progress achieved in pathological images is not remarkable, and detection and segmentation, which are among basic tasks of computer vision, are regarded as two independent tasks. In this paper, we make full use of existing datasets and construct a triplet-path network using dilated convolutions to cooperatively accomplish one-stage object detection and nuclei segmentation for general pathological images. First, in order to meet the requirement of detection and segmentation, a novel structure called triplet feature generation (TFG) is designed to extract high-resolution and multiscale features, where features from different layers can be properly integrated. Second, considering that pathological datasets are usually small, a location-aware and partially truncated loss function is proposed to improve the classification accuracy of datasets with few images and widely varying targets. We compare the performance of both object detection and instance segmentation with state-of-the-art methods. Experimental results demonstrate the effectiveness and efficiency of the proposed network on two datasets collected from multiple organs.

Fingerprint template protection has recently drawn much attention due to public security and privacy awareness. In this paper, we demonstrate an approach for designing a non-invertible fingerprint template by constructing the many-to-many bit-string polar transformations with different minutiae points. We explore the relative relationship of minutiae points in polar system, which is registration-free in practical implementations. The security is guaranteed by the non-invertibility of the polar transformations. The performance is evaluated for the merits of non-invertibility, accuracy, diversity and revocability using publicly available benchmark databases, FVC2002 (DB1, DB2) and FVC2004 (DB1, DB2). It shows that the proposed approach exhibits favorable performance compared with the state-of-art alignment-free cancelable fingerprint template protection scheme.

Femtocells can be employed as the low-power “green” wireless-access points to extend the macrocell network coverage and enhance the quality-of-service inside homes. In this paper, we focus on the joint subchannel allocation and transmitting-power control for femtocells during the downlink transmissions. Our objective is to minimize the total transmitting power across femtocell access points (FAPs) to facilitate “green” mobile communications. We would like to minimize the following criteria: (1) each user's received signal-to-interference-plus-noise ratio (SINR) per subchannel and (2) the number of subchannels each femtocell user requires to maintain a reliable quality-ofservice (QoS). Meanwhile, the interference of the users in the underlying femtocells to users in other macrocells should also be restricted. Thus, a new pertinent optimization problem is formulated as a mixed integer nonlinear program (MINLP), which is very complicated to solve in practice. In our work, a reformulation-linearization technique (RLT) based on a branchand-bound framework is invoked to simplify the aforementioned MINLP. Finally, simulation results are demonstrated for the effectiveness of our proposed new scheme.

Nowadays, it is well-known that the LTE (Long Term Evolution) networks have greatly tackled the Doppler effect problem at the physical layer since they are capable of achieving a 100 Mbps data throughput on a high-speed moving vehicle up to 350 km/h within the same cell (i.e. no handover is allowed). However, there are still cases where vehicles are moving at very high speeds and thus frequent handovers across cells are inevitable, such as high-speed trains, which have been constructed rapidly all over the world in recent years. Therefore, how to maintain good link quality and schedule multimedia services optimally on high-speed trains remains challenging. In this paper, we propose a novel optimal LTE-based multimedia-service-scheduling and resource-allocation mechanism for highspeed trains, which could maximize the service rate and maintain a good service quality at the same time. This new scheme makes use of the seamless handover mechanism by carefully organizing the cell array along the railroad and aggregating the data (at the femtocell) from different users within the train cabins. Besides, we also project to schedule the multimedia services and allocate the network resources as fair as possible for all the users on the train. The simulation results justify the effectiveness of our proposed new scheme.