Current spatial transcriptomics (ST) technology can integrate stained pathological slides with RNA sequencing, providing precise information on gene expression and cell types. However, the unique handling of pathological slides required by ST technology can lead to image quality issues, impacting model performance. To address this challenge, we applied Structure Preserving Color Normalization (SPCN), based on the Beer-Lambert law. Here, we use a pancreatic ductal adenocarcinoma (PDAC) dataset with class-level and gene-level annotations, and introduced Gene Feature Align (GFA) Loss to align classification features with gene expression, resulting in clearer decision boundaries. Our approach significantly enhances model performance on this dataset, offering novel solutions for spatial transcriptomics and paired pathological images, while contributing to limited research on PDAC slide cell distribution prediction through deep neural networks.

X-rays are widely used in clinical practice due to their low radiation exposure and cost. However, 2D imaging can result in overlapping anatomical structures. In contrast, CT scans generate 3D images, effectively addressing this limitation. Nevertheless, CT scans also have drawbacks, such as high radiation, high cost, and inability to be implemented within ICU settings. In this paper, we propose the AP2CT-GAN framework, which aims to reconstruct chest CT images from a single antero-posterior chest X-ray. The framework incorporates the Feature Enhancement Connection (FEC) and the Feature Dimension Converter (FDC) to enhance critical features and capture global contextual information, along with the Dual-Consistency Loss function propose to ensure that the reconstructed CT images maintain a high level of structural and textural consistency with the ground truth. Experimental results demonstrate that AP2CT-GAN outperforms existing methods, offering a low-radiation, low-cost CT imaging solution with valuable potential applications in resource-limited regions and ICU settings.

The detection of breast masses and calcified clusters on mammograms is critical for early diagnosis and treatment to improve the survivals of breast cancer patients. In this study, we propose a local-whole-focus pipeline to automatically identify breast masses and calcified clusters on full-size mammograms, from local breast tissues to the whole mammograms, and then focusing on the lesion areas. We first train a deep model to learn the fine features of breast masses and calcified clusteres on local breast tissues, and then transfer the well-trained deep model to identify breast masses and calcified clusteres on full-size mammograms with image-level annotations. We also highlight the areas of the breast masses and calcified clusteres in mammograms to visualize the identification results. We evaluated the proposed local-whole-focus pipeline on a public dataset CBIS-DDSM (Curated Breast Imaging Subset of Digital Database for Screening Mammography) and a private dataset MY-Mammo (Mianyang central hospital mammograms). The experiment results showed the DenseNet embedded with squeeze-and-excitation (SE) blocks achieved competitive results on the identification of breast masses and calcified clusteres on full-size mammograms. The highlight areas of the breast masses and calcified clusteres on the entire mammograms could also explain model decision making, which are important in practical medical applications.

Pneumothorax(PTX) is an emergency condition with lung collapse, causing breathing difficulties and life-threatening. It is necessary to segment PTX to help clinicians make decisions and guide subsequent treatment. General deep learning often requires a large dataset to improve the generalization ability of the model. However, it is arduous to obtain Computer Tomography (CT) PTX data with annotations. Automatic segmentation of CT-PTX has the problem of small samples, which cause unsatisfactory effect of existing methods. To against those problems, we proposed a self-supervised segmentation model for CT-PTX named SNU-Net (self-supervised nnU-net), which divided the segmentation task into pretext task and downstream task. In our proposed PNE dataset, segmentation accuracy was as high as 99.94%, and the dice coefficient reached 89.02%. Compared with the original nnU-net, our network only needs a few annotated data to achieve a better result.

With the development of mobile network and corresponding techniques, more and more works focus on providing efficient services based on mobile devices. Furthermore, motivated by IoT, studies of local distributed mobile devices attract attentions of both industry and academia in recent years. However, existing storage systems cannot manage data and support the QoS of mobile services well. This paper presents LKSM, a light weight key-value storage system, which can be deployed on either one node or multiple nodes. To the best of our knowledge, it is the first attempt to propose key-value store in this scenario. We carefully analyze the challenges when designing the system on mobile clusters, and further propose RDS for addressing. With the help of RDS, LKSM achieves the goal of lower latency, better scalability, and higher availability. Furthermore, based on RDS, a novel data management strategy is presented, which successfully avoid energy holes of mobile clusters and achieves the tradeoff between performance and energy. We organize LKSM using a log-structured merge-tree and implement it based on LevelDB, an open source key-value storage system proposed by Google. Experiments on physical smartphones demonstrate that LKSM presents much higher performance compared with the ported LevelDB on mobile devices.

Lung cancer is the leading cause of cancer-related deaths worldwide, while the risk factors for lung cancer mortality can be significantly reduced if the accurate early diagnoses for small malignant lung nodules are possible. In this paper, we propose a hierarchical attention-based multiple instance learning (HA-MIL) framework for patient-level lung cancer diagnosis by introducing two-level cascaded attention mechanisms, one at nodule level and the other at attribute level. The proposed HA-MIL framework is constructed by aggregating important attribute representation into nodule representation and then aggregating important nodule representation into lung cancer representation. The experiments on the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset showed that the HA-MIL model performed significantly better than the previous approaches such as the higher-order transfer learning, instance-space MIL and embedding-space MIL, which demonstrated the effectiveness of hierarchical multiple instance learning based on two-level attentions. The results analysis suggested that the HA-MIL model also found the key nodules and attributes by higher attention weights, which were more interpretable for the model decision making.

Early screening and diagnosis of breast mass are essential for the prevention of breast cancer. There are some reasons to make mass detection be difficult and challenging. First, the resolution of mammography is very large and mass tissues are often subtle. Second, some mass overlap with the normal tissues which own similar texture. In this paper, we propose a novel attention module channel self-attention block (CSABlock), it can make better use of inter-layer features and strengthen the detection capability of the cascade R-CNN model. In order to further improve the detection quality, we also use a new domain-adaptive pre-training strategy. Experiments show that the proposed method achieves an average precision (AP) of 0.822 and average recall (AR) of 0.949, outperforms the state-of-the-art methods.

Automatic pneumothorax segmentation on chest X-ray images is very crucial for diagnosis and treatment as large pneumothorax could be fatal. The pneumothorax segmentation is challenging, as some small pneumothoraces can be subtle, and may overlap with the ribs and clavicles. Meanwhile, the shape variation of pneumothorax is also very large, which also makes the segmentation more difficult. In this paper, we propose a novel automated pneumothorax segmentation framework which consists of three modules: 1) a fully convolutional DenseNet (FC-DenseNet), 2) a spatial and channel squeeze and excitation module (scSE), and 3) a multi-scale module. In order to improve boundary segmentation accuracy, a novel spatial weighted cross-entropy loss function is proposed, which penalize the target, background and contour pixels with different weights. Extensive experiments are conducted on the 2213 chest X-ray images of testing data and the results suggest that proposed segmentation algorithm outperforms the state-of-the-art methods in terms of mean pixel-wise accuracy (MPA) of 0.93±0.13 and dice similarity coefficient (DSC) of 0.92±0.14 etc. Accordingly, the effectiveness of our method is corroborated.

Attributes like texture, lobulation, malignancy, etc., are commonly used to describe the phenotype of a pulmonary nodule in computed tomography (CT) image, which can provide useful medical knowledge for the identification of early stage lung cancer. There may exist certain relations among these attributes, and some attributes may naturally imply or boost others that have been less comprehensively exploited in previous studies. In this paper, we explicitly model the relations among 11 attributes of nodules by way of transfer learning and extract a meta-structure that captures the transferabilities across deep features of these attributes. Specifically, a higher-order transfer learning scheme is proposed by involving three phases, i.e., semantic attribute-specific modeling, semantic attributes transfer modeling and pathologic attribute generalizing, to explore the strongest association across various attributes and to boost the nodule attribute predictions in chest CT images. The proposed approach has been evaluated on the 2632 nodules in the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset. The experimental results suggest that our higher-order transfer approach shows the superior predictive performance not only in the most of the semantic attributes compared with the schemes of learning from scratch and the first-order transfer but also for the pathologic attribute compared with the related studies. In addition, we demonstrate an attribute transfer graph to reveal which attributes combination can supply the most useful information to boost the predictive performance of target attributes.

The Smith-Waterman algorithm, which produces the optimal local alignment between pairwise sequences, is universally used as a key component in bioinformatics fields. It is more sensitive than heuristic approaches, but also more time-consuming. To speed up the algorithm, Single-Instruction Multiple-Data (SIMD) instructions have been used to parallelize the algorithm by leveraging data parallel strategy. However, SIMD-based Smith-Waterman (SW) algorithms show limited scalability. Moreover, the recent next-generation sequencing machines generate sequences at an unprecedented rate, so faster implementations of the sequence alignment algorithms are needed to keep pace. In this paper, we present CloudSW, an efficient distributed Smith-Waterman algorithm which leverages Apache Spark and SIMD instructions to accelerate the algorithm. To facilitate easy integration of distributed Smith-Waterman algorithm into third-party software, we provide application programming interfaces (APIs) service in cloud. The experimental results demonstrate that 1) CloudSW has outstanding performance and achieves up to 3.29 times speedup over DSW and 621 times speedup over SparkSW. 2) CloudSW has excellent scalability and achieves up to 529 giga cell updates per second (GCUPS) in protein database search with 50 nodes in Aliyun Cloud, which is the highest performance that has been reported as far as we know.

The clinical decision support system can effectively solve the limitations of doctors' knowledge, reduce misdiagnosis and help enhance health. The traditional genetic data storage and analysis technology based on the stand-alone environment have limited scalability, which has been difficult to meet the computational requirements of rapid genetic data growth. In this paper, we propose a distributed gene clinical decision support system, which is named as GCDSS. We implemented a prototype based on cloud computing. To speed up the data processing of GCDSS, we present a novel distributed read mapping algorithm CloudBWA that leverages batch processing strategy to map reads on Apache Spark. Evaluations show that GCDSS and its component CloudBWA achieve outstanding performance and excellent scalability. Compared with distributed algorithms, CloudBWA achieves up to 2.63 times speedup over SparkBWA.

Sequence alignment algorithms are a basic and critical component of many bioinformatics fields. With rapid development of sequencing technology, the fast growing reference database volumes and longer length of query sequence become new challenges for sequence alignment. However, the algorithms have prohibitively high time and space complexity. In this paper, we present DSA, a scalable distributed sequence alignment system that employs Apache Spark to process sequences data in a horizontally scalable distributed environment, and leverages data parallel strategy based on Single Instruction Multiple Data (SIMD) instruction to parallelize the algorithms in each core of worker node. The experimental results demonstrate that 1) DSA has outstanding performance and achieves up to 201x speedup over SparkSW. 2) DSA has excellent scalability and achieves near linear speedup when increasing the number of nodes in cluster.

With the development of deep learning and artificial intelligence, more and more research apply neural networks to natural language processing tasks. However, while the majority of these research take English corpus as the dataset, few studies have been done using Chinese corpus. Meanwhile, Existing Chinese processing algorithms typically regard Chinese word or Chinese character as the basic unit but ignore the deeper information into the Chinese character. In Chinese linguistic, strokes are the basic unit of Chinese character who are similar to letters of the English word. Inspired by the recent success of deep learning at character-level, we delve deeper to Chinese stroke level for Chinese language processing and developed it into service for Chinese text classification. In this paper, we dig the basic feature of the strokes considering the similar Chinese character components and propose a new method to leverage Chinese stroke for learning the continuous representation of Chinese character and develop it into a service for Chinese text classification. We develop a dedicated neural architecture based on the convolutional neural network to effectively learn character embedding and apply it to Chinese word similarity judgment and Chinese text classification. Both experiments results show that the stroke level method is effective for Chinese language processing.