Cervical cancer is a significant global health concern for women. Optical coherence tomography (OCT) offers a non-invasive, high-resolution imaging method for cervical examinations. The clinical need for real-time AI-aided diagnosis in low-resource settings necessitates model compression for deep learning models. Therefore, we develop a novel activation map-based knowledge distillation (AMKD) framework to address this issue. The AMKD framework ensures that the compressed (student) model achieves classification performance approximating that of the teacher model while maintaining the same activation area, enhancing interpretability for gynecologists. Due to the lack of high-quality annotated data, we also utilize unlabeled images for self-supervised distillation pre-training to improve model performance. On an internal dataset, the compressed models based on ResNet, ConvNeXt, and Swin-Transformer outperformed existing knowledge distillation frameworks while demonstrating better interpretability. On two external validation sets, the best compressed (ResNet) model surpassed the average performance of four medical experts in sensitivity and negative predictive value for cervical OCT volume classification, showcasing enhanced lesion detection capabilities. For a cervical OCT image of 760 × 1200 pixels, the compressed ResNet model with 1.37 M parameters uses 1.13 GB GPU memory during inference and predicts the input image in 0.06 seconds, potentially improving real-time detection of cervical lesions in resource-limited clinical settings.

Optical coherence tomography (OCT) emerged as a promising, non-invasive, real-time, high-resolution imaging technology for cervical cancer detection. The scarcity of high-quality annotated cervical OCT images severely hinders the predictive performance of deep learning models. In contrast, self-supervised learning (SSL) methods offer a viable solution to the above challenge. This study aims to develop a cost-effective SSL framework that efficiently classifies high-resolution cervical OCT images to meet the clinical "see-and-diagnose" requirement for cervical lesions. Therefore, we propose COVE, a novel SSL framework with masked autoencoders based on VMamba with linear complexity. To our knowledge, COVE is the first SSL framework using masked image modeling for VMamba to leverage large unlabeled cervical OCT datasets. It has two specific designs: (1) 2D-Selective-Scan for visible patches (VP-SS2D): the VMamba encoder’s core module processes only visible patches for efficient pre-training and addressing the inconsistency between pre-training and fine-tuning; (2) visible patch feature-preserving (VPFP): visible patch features in each decoder block are replaced with encoder-extracted features, decoupling the encoder’s feature extraction from the decoder’s pixel reconstruction tasks. Experimental results showed that COVE outperformed existing SSL frameworks in five-fold cross-validation on a 1,452-subject cervical OCT dataset from a multi-center study and two external validation sets from top Chinese hospitals. Additionally, COVE demonstrated the highest pre-training efficiency, with significantly faster speed than existing SSL frameworks.

Optical coherence tomography (OCT) is a three-dimensional laminar imaging technique that has recently been applied to gynecologic cervical lesions and has clinically proven its superior diagnostic performance to colposcopy. However, most gynecologists are not familiar with this new imaging technique and require longer specialized training to perform accurate interpretation, so there is a great need for efficient computer-aided diagnostic systems. We aim to study a deep learning model based on self-supervised learning, which mainly takes a contrast learning approach, and combines an attention-transferring feature extraction method with a model named multi-layer feature refinement extraction with contrastive learning (FRCL) to improve the accuracy of feature extraction, in addition to using a CNN network as the backbone. Our dataset is OCT images of the uterine cervix from 733 patients in China, and we compare the classification accuracy of our proposed model with the existing state-of-the-art supervised networks, CNN-based self-supervised networks and find that the accuracy is higher, with the AUC of 0.9789±0.0098 for dichotomous classification, the specificity of 93.44±5.13 and the sensitivity of 91.38±2.62. In the test, our model was about the same as the average diagnosis of medical experts. Also, on the external dataset, our model plays a stable level as usual and has good results in feature extraction and lesion identification.

Cervical cancer seriously endangers the health of the female reproductive system and even risks women's life in severe cases. Optical coherence tomography (OCT) is a non-invasive, real-time, high-resolution imaging technology for cervical tissues. However, since the interpretation of cervical OCT images is a knowledge-intensive, time-consuming task, it is tough to acquire a large number of high-quality labeled images quickly, which is a big challenge for supervised learning. In this study, we introduce the vision Transformer (ViT) architecture, which has recently achieved impressive results in natural image analysis, into the classification task of cervical OCT images. Our work aims to develop a computer-aided diagnosis (CADx) approach based on a self-supervised ViT-based model to classify cervical OCT images effectively. We leverage masked autoencoders (MAE) to perform self-supervised pre-training on cervical OCT images, so the proposed classification model has a better transfer learning ability. In the fine-tuning process, the ViT-based classification model extracts multi-scale features from OCT images of different resolutions and fuses them with the cross-attention module. The ten-fold cross-validation results on an OCT image dataset from a multi-center clinical study of 733 patients in China indicate that our model achieved an AUC value of 0.9963 ± 0.0069 with a 95.89 ± 3.30% sensitivity and 98.23 ± 1.36 % specificity, outperforming some state-of-the-art classification models based on Transformers and convolutional neural networks (CNNs) in the binary classification task of detecting high-risk cervical diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer. Furthermore, our model with the cross-shaped voting strategy achieved a sensitivity of 92.06% and specificity of 95.56% on an external validation dataset containing 288 three-dimensional (3D) OCT volumes from 118 Chinese patients in a different new hospital. This result met or exceeded the average of four medical experts who have used OCT for over one year. In addition to promising classification performance, our model has a remarkable ability to detect and visualize local lesions using the attention map of the standard ViT model, providing good interpretability for gynecologists to locate and diagnose possible cervical diseases.

Isostatic compensation is a crucial component of crustal structure analysis and geoid calculations in cases of gravity reduction. However, large-scale and high-precision calculations are limited by the inefficiencies of the strict prism method and the low accuracy of the approximate calculation formula. In this study, we propose a new method of terrain grid re-encoding and an eight-component strict prism integral disassembly using a compute unified device architecture parallel programming platform. We use a fast parallel algorithm for the isostatic compensation correction, using the strict prism method based on CPU + GPU heterogeneous parallelization with efficient task allocation and GPU thread overloading procedure. The results of this study provide a rigorous, fast, and accurate solution for high-resolution and high-precision isostatic compensation corrections. To ensure an absolute calculation accuracy of 10−6 mGal, the maximum acceleration ratio of the calculation was set to at least 730 using one GPU and 2241 using four GPUs, which shortens the calculation time and improves the calculation efficiency.

Abstract In an attribute‐based signature (ABS), users sign signatures based on some predicate of attributes, using keys issued by a central authority. A signature reveals nothing about the attributes of the signer beyond the fact that they satisfy the signing predicate. This paper presents an ABS scheme for the case of threshold predicates from lattices. This scheme is existentially unforgeable against selective predicate and static chosen message attacks in the standard model, with respect to the hardness of the small integer solution problem. To the best of our knowledge, this work constitutes the first ABS scheme based on lattices, which is conjectured to thwart the quantum threat. Copyright © 2014 John Wiley & Sons, Ltd.