The diagnosis of high myopia using fundus images is essential for visual health. Existing deep learning-based methods rely on large-scale labeled data, but annotated data for high myopia fundus images remains scarce. To address this issue, we mimic the ability of ophthalmologists to diagnose a new disease with only a small number of samples. In this regard, we propose a meta-learning network guided by domain knowledge from fundus images for diagnosing high myopia. The model consists of three modules: First, the image reconstruction module builds an autoencoder (comprising an encoder and a decoder) that takes the input raw image and outputs the reconstructed image. Next, the fundus image domain knowledge learning module constructs a Siamese network to learn the similarity between the original and reconstructed fundus images. This similarity is used as a loss function to guide the encoder in effectively learning the domain features of fundus images. Finally, in the domain-knowledge-guided meta-learning module, the encoder’s initialization parameters (obtained from the first two modules) are further optimized using the OCMAMAL architecture, resulting in more optimal encoder parameters. This optimization helps achieve superior recognition performance with only a small amount of data for new tasks. Using a clinically real high myopia fundus image dataset, our method achieved F1 scores of 83.4%, 87.9%, and 89.2% under 5-shot, 10-shot, and 20-shot conditions, respectively, demonstrating the effectiveness of the proposed method.

Glaucoma is a chronic and irreversible eye disease. Early detection and treatment can effectively prevent severe consequences. Deep transfer learning is widely used in fundus imaging analysis to remedy the shortage of training data of glaucoma. The model trained on the source domain may struggle to predict glaucoma in the target domain due to distribution differences. Several limitations cannot be ignored: (1) Image matching: enhancing global and local image consistency through bidirectional matching; (2) Hierarchical transfer: developing a strategy for transferring different hierarchical features. To this end, we propose a novel Matched Hierarchical Transfer Network (MHT-Net) to achieve automatic glaucoma detection. We initially create a fundus structure detector to match global and local images using intermediate layers of a pre-trained diagnostic model with source domain data. Next, a hierarchical transfer network is implemented, sharing parameters for general features and using a domain discriminator for specific features. By integrating adversarial and classification losses, the model acquires domain-invariant features, facilitating precise and seamless transfer of fundus information from source to target domains. Extensive experiments demonstrate the effectiveness of our proposed method, outperforming existing glaucoma detection methods. These advantages endow our algorithm as a promising efficient assisted tool in the glaucoma screening.

Non-invasive fundus images can be used to diagnose various fundus diseases, such as high myopia (HM). Existing deep learning-based research mainly relies on data to drive the model to learn key features. However, the data related to HM is limited (especially for young children), making it difficult for deep networks to accurately focus on key features. Hence, we propose a prior knowledge-guided deep learning network for pediatric HM detection. It comprises four modules: (1) Prior Feature-Based Channel Fusion: This module extracts key features (brightness, edges, texture) from fundus images using image processing methods to obtain corresponding single-channel slices. Through channel-level feature fusion, these slices are used to construct multiple sets of feature-enhanced datasets. (2) Global Fundus Feature Extraction: It uses residual blocks to build the backbone, and builds aU-shaped attention component based on the U -shaped network. This module extracts the global and context information of the original fundus image to obtain a global feature map. (3) Knowledge-Guided Attention Generation: The residual structure is employed to further extract the hidden features of the feature-enhanced data, thereby obtaining local key feature maps. (4) Pediatric HM Classification: By combining local key feature maps (obtained in module 3) with global feature maps (obtained in module 2) through spatial attention mechanism, the deep network is guided to complete the classification task of pediatric HM. Extensive experiments on real-world datasets demonstrate the effectiveness of our method (accuracy is 0.921, F1 score is 0.903).

Breast cancer has become the primary cancer endangering women's life and health. A large number of evidence based on medicine show that early screening can effectively reduce the mortality rate of breast cancer. With the development of computer vision technology, the computer-aided diagnosis system for breast cancer screening and detection has attracted extensive attention from all walks of life and breast lesion localization and diagnosis are the key steps. Therefore, this article starts from the perspective of model feature construction to conduct an in-depth analysis and a comprehensive summary of the existing research on breast image lesion localization and benign-malignant diagnosis. This article divides feature construction methods into three types based on novel perspectives: domain knowledge-driven, data-driven, and domain knowledge-driven fusion data-driven. On this basis, the article uses a systematic review method to classify, summarize, and compare the models for breast image lesion localization and diagnosis which greatly expands and deeply analyzes existing reviews. In addition, this article elaborates on the existing problems in current research work and discusses the future outlook of breast image lesion localization and benign-malignant diagnosis.

At the end of 2019, the COVID-19 outbreak emerged abruptly. Chinese health authorities highlighted the role of CT scans, X-rays, and other computerized lung imaging in aiding COVID-19 diagnosis. This study aims to develop a computer-based system to assist healthcare professionals in diagnosing COVID-19 infections based on computerized imaging analysis. This approach aims to alleviate the workload of COVID-19 specialists, improving diagnostic and treatment efficiency and allowing specialists to focus on devising appropriate patient care plans promptly. The proposed method focuses on analyzing COVID-19 lesion characteristics within individual CT slices and their serial characteristics across CT sequences. This approach mirrors the diagnostic process of radiologists closely. To validate our model, we compiled a dataset from real medical diagnostic settings, minimizing the impact of lesion-like artifacts. We conducted a series of comparative and ablation experiments to evaluate the model's performance. Results indicate that our model outperforms the classic classification models and other commonly used models for COVID-19 diagnosis on our constructed dataset.

Multi-class segmentation of eye images plays a pivotal role in assessing patients with myasthenia gravis, and the measurement results rely heavily on the segmentation accuracy. However, there is still a problem with inaccurate boundary segmentation. Compared to heuristic-based network structure optimization, exploring effective loss function is an intuitive, simple, and interpretable way to address this issue. In this paper, we experimentally verify the effectiveness of boundary loss for multi-class segmentation of eye images and investigate its hybrid law with other segmentation losses. The application of the study significantly enhances the accuracy of myasthenia gravis scoring and holds promise for assisting in the evaluation of various other eye diseases.

Sign language video translation, which converts sign language information into textual expressions, play a vital role in breaking down the language communication barrier between deaf and healthy people. Existing translation methods are mainly focus on the single and pure background. However, the background in real-world environments is always complex, and these methods are difficult to achieve effective recognition results. To address this issue, we have exploratively constructed a real-world complex background sign language dataset (CBSL), containing sign language videos captured in various authentic environments (e.g., different backgrounds and lighting conditions). Based on this, we propose a progressive sign language translation model to effectively separate sign language users from the background and reduce environmental interference, thus significantly improving the generalization ability. Our proposed method significantly outperforms various comparative methods across all performance metrics on the CBSL dataset. Furthermore, on the publicly available Chinese Sign Language Continuous Recognition dataset(CSL), our method performs comparably to the current state-of-the-art (SOTA).

Breast ultrasound (BUS) image lesion segmentation is an important step in computer-aided diagnosis (CAD) systems for breast cancer screening. Due to acquiring pixel-level labels for fully supervised BUS lesion segmentation being extremely expensive and time-consuming, many studies have adopted weakly supervised methods to mitigate the reliance of models on pixel-level labels. However, in weakly supervised methods, the class activation map (CAM) often excessively focuses on the most distinctive regions of the target while overlooking other lesion parts. This limitation often leads to insufficient CAM activation and compromises the accuracy of lesion segmentation. To address this problem, we propose a weakly supervised framework for lesion segmentation on BUS images using image-level labels. Specifically, the method consists of two stages: classification and segmentation. During the classification stage, the network based on the progressive soft erase (PSE) module reduces the contribution of the most discriminative feature region in CAM, making the network focus on the features of non-dominant regions and generating more high-quality pseudo-masks. In the segmentation stage, we propose a dual-branch feature fusion (DBFF) network, which can not only capture more contextual information by fusing from different scales but also generate a more accurate segmentation mask. Extensive experiments on the publicly available dataset BUS I show the effectiveness of our method. Furthermore, this model significantly supports medical education management by providing a more efficient and cost-effective approach to breast lesion segmentation.

Myasthenia gravis (MG) is a neurological disease that is difficult to diagnose and requires long-term management. The progression of this disease is reflected to some extent in changes in speech, such as hoarseness and articulation disorders. However, it is difficult for general neurologists to grasp the diagnostic patterns of such rare diseases, especially in underdeveloped regions. As an emerging field, speech-based intelligent diagnostic assistance provides a safe, non-invasive, and convenient solution for healthcare education management. To this end, we firstly constructed a novel Chinese speech dataset of myasthenia gravis patients (MGCS). Then we proposed a network named Myasthenia Gravis Speech Net (MGS-Net) for the classification of myasthenia gravis pathological speech, which is mainly composed of two blocks: the Local Feature Enhancement (LFE) block and the Feedforward Dense (FFD) block. The LFE block extracts temporal local features using a sliding window approach, while the FFD block captures the global representation of the data. Compared to existing methods, our pipeline achieves an accuracy of 98.75% and a recall rate of 99.17%. We validated the effectiveness of existing acoustic feature sets in pathological speech classification of MG, which will provide an important tool for health education management of neurological diseases.

Accurate lesion segmentation on breast ultrasound (BUS) images is a crucial procedure in computer-aided ultrasonic diagnosis. Owing to the privacy of BUS data and the complexity of acquiring pixel-level labels, numerous researches attempt to achieve breast lesion segmentation with predefined feature-based and deep learning-based methods in a unsuper-vised or weakly supervised scenario. Although the former can typically extract more reliable contour information of the lesion, it is severely interfered by irrelevant tissues due to its inability to capture any semantic information. Furthermore, the weakly supervised deep learning segmentation based on class activation map (CAM) can explore the semantic information while failing to provide precise contour information. In view of the above observation, we present a weakly supervised framework merging complementary contour and semantic information for early lesion segmentation in BUS images. Specifically, guided by the prior knowledge of breast anatomy, we first extract and filter the contour information of suspected lesions located in breast parenchyma layer by clustering and morphological characteristic, respectively. Afterward, semantic information extraction is performed by a classification network to automatically explore the category information of the lesion. Finally, we selectively fuse the complementary information to facilitate lesion segmentation performance with more comprehensive features. Extensive experiments are conducted on the public dataset BUSI, and the results confirm the validity of our approach.

The automatic disease diagnosis utilizing clinical data has been suffering from the issues of feature sparse and high probability of missing values. Since the graph neural network is a effective tool to model the structural information and infer the missing values, it is becoming the dominant method for the predictive model construction from electronic medical records. Existing graph neural network based solutions usually adopt the medical concepts (e.g., symptoms) the feature representation of clinical data without considering their underlying semantic relations. The limited discriminative capability of the medical concept cannot provide sufficient indicative information about the disease. This article proposes a knowledge-guided graph attention network for the disease prediction. Beside extracting the attribute-value structure as a large-size medical concept, the mutual information between multiple medical concepts mentioned in the electronic medical records are taken into account in the graph construction. Meanwhile, the defined diseases and their associations with the medical concepts in the medical knowledge graph are incorporated into the graph, which provides the potentials to enhance the indicative impacts of the medical concepts directly related to a target disease. Then, the spatial and attention based graph encoders are employed to aggregate information from directly neighbor nodes to generate node embeddings as the compact features to be used for disease diagnosis. The approach itself is a general one that can utilized to build the predictive model using Chinese EMRs for different diseases. The empirical experiments for its performance evaluation are conducted on the real-world COPD EMR dataset. The comparison study results show that the proposed model outperforms baseline methods, which illustrates the effectiveness of our proposed model.

Microscopic imaging plays an important role in the biomedical field. Existing deep learning based methods rely on high-quality data. However, there is a lot of noise (such as bubbles and impurities) in the microscopic images of biological samples collected outdoors, which may lead to significant interference in the microscopic objects identification task. To solve this problem, this paper proposes a deep learning based method for microscopic object localization and classification. Firstly, the whole slide image is preprocessed to obtain the microscopic images after preliminary filtering bubbles and impurities. Then, the sensitized pollen grains are located based on the deep learning model to remove the interference of remaining impurities, and the microscopic images of sensitized pollen grains are classified. This method can effectively suppress the interference of noise in microscopic images on object classification and improve the accuracy and reliability of model. The proposed method is verified by experiments based on real data and the results show that the proposed method achieves the highest accuracy compared with other deep learning methods.

Pollen classification plays an essential role in many fields such as medicine and palynology. Notably, manual pollen identification via observing key pollen information (e.g., their contours) is time-consuming and laborious. To date, deep learning methods can extract complex features in an end-to-end manner. However, deep learning based automatic classification methods on pollen grains are still rare, and their performances remain unsatisfactory owing to limitation of interference from irrelevant information (such as impurities and bubbles) and the lack of pollen attention. Based on the above considerations, we propose a contour-guided network called CG-Net, which contains three modules. Image pre-processing module first removes impurities and bubbles in pollen images according to the color information. Then, contour awareness module is designed to generate contour features and these features are served as attention maps for next module. Finally, contour guidance module weights the yielded contour attention maps to both original images and feature maps of different convolution layers, making the CNN focus on discriminative features of pollen grains. Extensive experiments are conducted on several real-world pollen datasets, and the results demonstrate the effectiveness of our proposed method with the accuracy and F1-score over 84%.

Cataract is a chronic eye disease that causes irreversible vision loss. Automatic cataract detection can help people prevent visual impairment and decrease the possibility of blindness. To date, many studies utilize deep learning methods to grade cataract severity on fundus images. However, they mainly focus on the classification performance and ignore the model interpretability, which may lead to a semantic gap between networks and users. In this paper, we present a deep learning network to improve the model interpretability, which consists three main modules: deep feature extraction, visual saliency module and semantic description module. Visual and semantic interpretation jointly employed to provide cataract-grade oriented interpretation for the overall model. Experimental results on real clinical data set show that our method improves the interpretability for cataract grading while ensuring the high classification performance.

The murine model serves as an important experimental system in biomedical science because of its high degree of similarities at the sequence level with human. Recent studies have compared the transcriptional landscapes between human and mouse, but the general co-expression landscapes have not been characterized. Here, we calculated the general co-expression coefficients and constructed the general co-expression maps for human and mouse. The differences and similarities of the general co-expression maps between the two species were compared in detail. The results showed low similarities in the human and mouse, with only about 36.54% of the co-expression relationships conserved between the two species. These results indicate that researchers should pay attention to these differences when performing research using the expression data of human and mouse. To facilitate use of this information, we also developed the human-mouse general co-expression difference database (coexpressMAP) to search differences in co-expression between human and mouse. This database is freely available at http://www.bioapp.org/coexpressMAP.

At present, understanding of DNA methylation at the population level is still limited. Here, we first extended the classical framework of population genetics, such as single nucleotide polymorphism allele frequency, linkage disequilibrium (LD), LD block and haplotype, to epigenetics. Then, as an example, we compared the DNA methylation disequilibrium (MD) maps between HapMap CEU (Caucasian residents of European ancestry from Utah) population and YRI (Yoruba people from Ibadan) population (lymphoblastoid cell lines). We analyzed the differences and similarities between CEU and YRI from the following aspects: SMP (single methylation polymorphism) allele frequency, SMP allele association, MD, MD block and methylation haplotype (meplotype) frequency. The results showed that CEU and YRI had similar distribution of SMP allele frequency, and shared many MD block region. We believe that the framework of population genetics can be used in the population epigenetics. The population epigenetic framework also has potential prospects in the study of complex diseases, such as epigenome-wide association study.

Motivation: With the development of biotechnology, DNA methylation data showed exponential growth. Epigenome-wide association study (EWAS) provide a systematic approach to uncovering epigenetic variants underlying common diseases/phenotypes. But the EWAS software has lagged behind compared with genome-wide association study (GWAS). To meet the requirements of users, we developed a convenient and useful software, EWAS2.0. Results: EWAS2.0 can analyze EWAS data and identify the association between epigenetic variations and disease/phenotype. On the basis of EWAS1.0, we have added more distinctive features. EWAS2.0 software was developed based on our 'population epigenetic framework' and can perform: (i) epigenome-wide single marker association study; (ii) epigenome-wide methylation haplotype (meplotype) association study and (iii) epigenome-wide association meta-analysis. Users can use EWAS2.0 to execute chi-square test, t-test, linear regression analysis, logistic regression analysis, identify the association between epi-alleles, identify the methylation disequilibrium (MD) blocks, calculate the MD coefficient, the frequency of meplotype and Pearson's correlation coefficients and carry out meta-analysis and so on. Finally, we expect EWAS2.0 to become a popular software and be widely used in epigenome-wide associated studies in the future. Availability and implementation: The EWAS software is freely available at http://www.ewas.org.cn or http://www.bioapp.org/ewas.

We present a novel standard convolutional symbols generator (SCSG) block for a multi-parameter reconfigurable Viterbi decoder to optimize resource consumption and adaption of multiple parameters. The SCSG block generates all the states and calculates all the possible standard convolutional symbols corresponding to the states using an iterative approach. The architecture of the Viterbi decoder based on the SCSG reduces resource consumption for recalculating the branch metrics and rearranging the correspondence between branch metrics and transition paths. The proposed architecture supports constraint lengths from 3 to 9, code rates of 1/2, 1/3, and 1/4, and fully optional polynomials. The proposed Viterbi decoder has been implemented on the Xilinx XC7VX485T device with a high throughput of about 200 Mbps and a low resource consumption of 162k logic gates.